Managing energy assets associated with transport operations

ABSTRACT

Apparatus, systems, and methods are described that can be used to generate an operating schedule for a controller of an energy storage asset that is in communication with a transport vehicle, based on an optimization process. The operating schedule is generated based on an operation characteristic of the energy storage asset, an energy-generating capacity of the transport vehicle in communication with the energy storage asset based on a motion of the transport vehicle, and a price associated with a market (including a regulation market and/or an energy market). Operation of the energy storage asset according to the generated operating schedule facilitates derivation of energy-related revenue, over a time period T. The energy-related revenue available to the energy customer over the time period T is based at least in part on the regulation market and/or the energy market.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to and benefit of U.S. ProvisionalApplication No. 61/552,982, filed on Oct. 28, 2011, entitled “METHODS,APPARATUS, AND SYSTEMS FOR DETERMINING CHARGE/DISCHARGE SCHEDULES FORENERGY STORAGE ASSETS ASSOCIATED WITH TRANSPORTATION OPERATIONS TOFACILITATE REVENUE GENERATION FROM WHOLESALE ELECTRICITY MARKETS,” theentire disclosure of which is incorporated herein by reference in itsentirety, including drawings.

This application also claims priority to and benefit of U.S.Non-provisional application Ser. No. 12/850,918, filed on Aug. 5, 2010,which claims priority to U.S. Provisional Application No. 61/279,589,filed on Oct. 23, 2009.

This application also claims priority to and benefit of U.S.Non-provisional application Ser. No. 13/451,497, filed on Apr. 19, 2012,which claims priority to U.S. Provisional Application No. 61/477,067,filed on Apr. 19, 2011, and U.S. Provisional Application No. 61/552,982,filed on Oct. 28, 2011.

The entire disclosure of these applications is incorporated herein byreference in its entirety, including drawings.

BACKGROUND

In various regions across the United States, “regional transmissionoperators” (RTOs) or “independent system operators” (ISOs) generally areresponsible for obtaining electricity from electricity generators (e.g.,operators of coal-fired plants, gas plants, nuclear plants,hydroelectric plants, renewable resources, etc.), and then transmittingthe electricity provided by generators over particular geographicregions (e.g., New England, the greater New York area, the mid-Atlanticstates) via an electricity transmission infrastructure (also commonlyreferred to as the electricity “grid”). RTOs generally are responsiblefor regional planning of grid expansion and/or ordering deployment ofnew electricity transmission infrastructure by transmission owners.

The Federal Energy Regulation Commission (FERC) presently requires that,in addition to generally managing the operation of the electricity gridin a given geographic area, RTOs/ISOs need to manage the price ofelectricity generated and consumed on the grid via “wholesaleelectricity markets.” To this end, RTOs/ISOs establish pricing auctionsto provide and support wholesale electricity markets. These pricingauctions, in addition to setting wholesale prices as a function of time,also foster sufficient electricity production for the grid at variouslocations to ensure that the grid is capable of delivering adequateelectricity to respective locations of demand for electricity on thegrid. Thus, some of the key objectives of the RTOs/ISOs in overseeingwholesale electricity markets include providing for efficient, economicand reliable operation of the grid.

In general, a given RTO/ISO supports a wholesale electricity market byallowing competing electricity generators to offer their electricityproduction output to the RTO/ISO. Retail electricity suppliers, alsocommonly referred to as “utilities,” in turn supply electricity toend-users/consumers, or “energy customers” of the retail electricitysuppliers, and are billed by the RTO/ISO for their purchases. Withrespect to the wholesale electricity market, the retail electricitysuppliers make bids for the electricity production output offered by theelectricity generators that, once accepted, establish market prices. Theretail electricity suppliers in turn typically re-price the electricitythey purchase from electricity generators on the wholesale market tosell to their retail electricity customers.

One significant issue facing RTOs/ISOs relates to various limitationsthat exist in connection with the grid that may impede a sufficient flowof electricity on the grid under certain circumstances. In particular,there may be time-dependent and/or geographically-dependent limitationson the grid's ability to support transmission of electricity, based onone or more of: 1) an available overall supply of electricity fromelectricity generators; 2) overall demand from retail electricitysuppliers; 3) general conditions on the grid itself (e.g., aging,failing or dated equipment); and 4) “location-specific” or “congestion”issues, e.g., respective geographic locations on the grid of electricitygenerators, electricity consumers, particular demand conditions, and/orparticular grid-related conditions that in some manner impede thetransmission of available electricity to one or more portions of thegrid). In some circumstances, a grid limitation may be caused by aparticular branch of the grid reaching a thermal limit, or a failure ofa generator or transformer on a branch of the grid; these limitationsgenerally are referred to as “security constraints” (i.e., particulargrid infrastructure cannot be overloaded without jeopardizing the grid).As such, the electricity grid is sometimes referred to as a “securityconstrained system.”

In view of the foregoing, RTOs/ISOs may employ a process known as“security constrained economic dispatch” for establishing wholesaleelectricity prices on a wholesale electricity market. Pursuant to thisprocess, an RTO/ISO managing a particular geographic region of anelectricity grid determines particular locations on the grid, or“nodes,” at which there is a possibility for security constraints tolimit electricity transmission. Wholesale electricity prices as afunction of time are then established independently for each node (i.e.,on a geographically-dependent, or “locational” basis) by accepting bidsfrom energy generators in sequence from the lowest priced offer to thehighest priced offer, up to an amount of electricity needed to satisfyelectricity demand conditions (e.g., bids from retail electricitysuppliers) at the node, so as to develop a supply and demand equilibriumprice. In this manner, the wholesale electricity price at a particularnode reflects the highest-priced accepted generation offer needed toprovide an adequate amount of electricity to that node, taking intoconsideration various security constraints that may be present at thenode. This location-based approach to wholesale electricity prices,which takes into consideration security constraints on the grid,commonly is referred to as “locational marginal pricing,” and thewholesale electricity price at a given node is commonly referred to aLocational Marginal Price (LMP). Thus, the wholesale electricity pricegenerally varies at different locations on the grid, based at least inpart on security constraints.

While electricity generators and retail electricity suppliers make up asignificant constituency of the participants in wholesale electricitymarkets, applicable market rules in some wholesale electricity marketsalso permit electricity consumers/end-users (e.g., energy customers ofretail electricity suppliers) and others to participate in wholesaleelectricity markets so as to earn energy-related revenue and offsettheir energy-related expenditures. In particular, market rules nowpermit energy users (or their market representatives) to make offers tocurtail or otherwise alter their electricity use, or to sellself-generated or stored electricity, to the wholesale market. If suchan offer by an energy customer to provide an “electricity-relatedproduct or service” is accepted on the applicable wholesale market, thecustomer endeavors to appropriately control its various energy assets soas to make available to the grid the offered product/service, in returnfor payment pursuant to the terms of the offer. The concept of an energycustomer providing an electricity-related product or service (e.g.,electricity use curtailment) on a wholesale electricity market inexchange for payment to the energy customer by the RTO/ISO, commonly isreferred to as “demand response” (DR).

Some of the currently more active wholesale electricity sub-markets inwhich energy customers of retail service providers may readilyparticipate include the “energy markets” (e.g., “day-ahead” energymarket, “real-time dispatched” energy market). While various pricingmodels exist for participation in these markets and other economicdemand response wholesale electricity markets (as well as variouspenalty models for customer non-performance pursuant to an offer toreduce/curtail energy use), often any revenue generated by the energycustomer from participation in these markets is based on the locationalmarginal price (LMP). The LMP may be calculated periodically atspecified nodes (e.g., every 5 minutes, every half-hour, every hour)depending on the particular market in which the energy customer isparticipating. More generally, revenue generation relating toparticipation in an economic demand response wholesale electricitymarket is based on a prevailing “wholesale electricity price” for theparticular market in question, which in turn generally is based on theLMP (calculated at various intervals), as discussed above.

To determine revenue earned by participating energy customers in aparticular economic demand response wholesale electricity market such asan “energy market,” the amount of electricity use reduction by theparticipating customer typically has to be measured; subsequently, thismeasured amount of electricity use reduction typically is multiplied bya price relating to the prevailing wholesale electricity price for themarket in question (e.g., LMP). Electricity use reduction by the energycustomer conventionally is measured against a reference electricityusage commonly referred to as a “customer baseline” (CBL). The CBL isintended to represent what the participating energy customer'selectricity use normally would have been, over a particular time periodand typical (“business-as-usual” or BAU) operating conditions for thecustomer's energy assets, absent the customer's voluntary electricityuse reduction based on the incentive provided by the economic demandresponse wholesale electricity market.

Conventionally, a customer baseline (CBL) electricity use profile for anenergy customer is derived by an RTO/ISO from an historical sample ofactual electricity use by the customer over a particular time period andBAU operating conditions. In some cases, the particular time period forwhich an historical sample of the customer's actual electricity use isselected as a CBL may be based, at least in part, on similar conditionsprevailing at the customer's site at the time of the historical samplingand participation in the economic demand response program (e.g., similarweather conditions, similar seasons/time of year, similar occupancyconditions at the customer's site, etc.). In other instances, the timeperiod for selecting an historical sample of actual electricity usage asa CBL is based on relatively recent actual electricity use by the energycustomer just prior to the customer's participation in the economicdemand response program. For example, the ISO PJM Interconnectcalculates a market-participating customer's CBL for a given weekday as“the average of the highest four out of the five most recent highestload (electricity use) weekdays in the 45 calendar day period precedingthe relevant load reduction event.” In sum, revenue generation from theeconomic demand response wholesale electricity “energy markets”conventionally is based on an historical actual electricity usage of aparticipating customer, which historical actual electricity usage servesas a customer baseline (CBL) against which electricity use reduction ismeasured for purposes of paying the energy customer for the usereduction.

SUMMARY

The Inventors have recognized and appreciated that new opportunities forparticipation in wholesale electricity markets by electricityconsumers/end-users (e.g., energy customers of retail electricitysuppliers) have created a need for energy management tools to facilitateenergy-related revenue generation from such markets. In view of theforegoing, various embodiments are directed generally to methods,apparatus and systems for determining operating schedules for energyassets so as to facilitate revenue generation from wholesale electricitymarkets. These energy assets include energy storage assets, energyconsuming assets and energy generating assets. In different examplesherein, an energy asset can include an energy storage asset, an energyconsuming asset, and/or an energy generating asset.

Wholesale electricity markets in which the energy customer mayparticipate to earn energy-related revenue, and to which the variousmethods, apparatus and systems according to the concepts disclosedherein may apply, include various economic demand response wholesaleelectricity markets, examples of which include, but are not limited to,a “real-time energy market,” a “day-ahead energy market,” a “day-aheadscheduling reserve market,” a “synchronized reserve” market, a“regulation” market, a “capacity” market, and an “emissions” market. Thevarious methods, apparatus and systems according to the conceptsdisclosed herein may also apply to facilitate the energy customerparticipating in a market based on a voltage/VAR ancillary service toearn energy-related revenue. In some examples, the methods, apparatusand systems described herein may be implemented in whole or in part by acurtailment service provider (CSP) or other entity acting as a “broker”between energy customers and an RTO/ISO to facilitate participation invarious demand response programs supported by wholesale electricitymarkets.

Suggested Operating Schedules for Energy Assets

In example implementations discussed in greater detail below, themethods, apparatus and systems described herein determine a suggestedoperating schedule for one or more energy assets (includingenergy-consuming assets for which energy usage may be curtailed), over agiven time period T, that are operated by an energy customer of a retailelectricity supplier. The energy assets operated by the energy customermay include electricity-consuming assets as well aselectricity-generating assets (e.g., fossil-fuel-based generators,renewable energy sources) and/or electricity storage assets (e.g.,batteries). The time period T over which a suggested operating schedulefor the energy asset(s) may be determined according to the inventiveconcepts disclosed herein may be a portion of an hour, an hour, a periodof multiple hours, a day, or a period of multiple days, for example(which in some instances may be based, at least in part, on time-varyingwholesale electricity prices on a particular wholesale electricitymarket from which revenue may be generated). Similarly, the suggestedoperating schedule(s) for the energy assets(s) may be determined basedat least in part on wholesale prices of various wholesale electricity“products” offered on the wholesale electricity markets in which theenergy customer may participate (e.g., based on a geographic region inwhich the energy customer is located) to earn energy-related revenue.

In one example implementation, as discussed in greater detail below, thesuggested operating schedule for one or more energy assets is determinedvia a mathematical optimization process that reduces a netenergy-related cost to the energy customer over the time period T byincreasing projected energy-related revenue from one or more wholesaleelectricity markets in which the energy customer may participate.

Energy Asset Modeling

To facilitate the mathematical optimization process for generating asuggested operating schedule for one or more energy assets, amathematical model representing the customer's energy asset(s) isformulated and employed in the mathematical optimization process. Theenergy asset model is specified by one or more mathematical functionsfor calculating an energy profile (i.e., electricity use and/orelectricity generation as a function of time over the time period T) forthe asset(s), based on a proposed operating schedule for the asset(s)applied as an input to the model. In one aspect, the mathematicalfunction(s) defining the asset model at least in part represent physicalattributes of the energy asset(s) themselves that relate to electricityuse and/or electricity generation. Depending on the energy asset(s)operated by the energy customer, a given model may represent a singleenergy asset or an aggregation of multiple energy assets operated by thecustomer.

Also, depending on the type of energy asset(s) being modeled, the assetmodel may be formulated to accept additional inputs to facilitatecalculation of an energy profile based on a proposed operating schedule.Herein, in various examples, energy storage assets, energy consumingassets and/or energy generating assets are being modeled. For example,in the case of energy consuming assets such as transport vehiclesincluding heating, ventilation and air conditioning (HVAC) systems fortemperature control in one or more buildings, and/or other assets(including transport vehicles) for which thermodynamic considerationsare relevant (including weather- or temperature-dependent energygenerating assets including photovoltaic cells and wind turbines), themathematical model for the asset(s) may be configured to consider as aninput to the model actual or forecast ambient environmental conditions(e.g., temperature, humidity, ambient light/cloud cover, etc.) as afunction of time, as well as other variables that may impactthermodynamics or the energy profile in general (e.g., occupancy, apresence of equipment such as computers and other instrumentation thatmay affect heating or cooling in an environment, etc.).

Customer Baseline (CBL) Energy Profiles for Business-As-Usual (BAU)Operating Schedules

In some examples, the mathematical model for the energy asset(s) firstis used to generate a simulated (or “predictive”) customer baseline(CBL) energy profile corresponding to a typical operating schedule (alsoreferred to herein as a “business-as-usual” (BAU) operating schedule, or“BAU conditions”). In particular, an energy customer's BAU operatingschedule for its energy asset(s) is applied to the mathematical model,which in turn provides as an output a simulated CBL energy profilerepresenting a typical electricity consumption or generation as afunction of time, over a given time period T, for the modeled energyasset(s). In one aspect, the energy customer's BAU operating schedulerepresents the customer's typical behavior with respect to operating itsenergy asset(s), absent any incentive to reduce energy costs and/or earnenergy-related revenue from the wholesale electricity market.

As discussed in greater detail below, a simulated and predictive CBLenergy profile based on a mathematical model according to the conceptsdisclosed herein provides a significant improvement over conventionalapproaches to determine a frame of reference for typical energy profilesof energy customers (absent an incentive to generate revenue viawholesale electricity markets); as noted above, conventional approachesare limited to considering only historical actual energy useinformation. In particular, it is recognized and appreciated herein thatconventional backward-looking assessment of CBL is not necessarilyrepresentative of what an energy customer's electricity usage actuallywould have been on a given day for which economic demand responserevenue is being calculated—at best, such backward-looking historicalactual-use-based assessments of CBL provide inconclusive estimates.

Additionally, it has been observed empirically that an historicalactual-use CBL provides incentives for some energy customers toartificially inflate energy usage (i.e., by not operating energy assetspursuant to “business-as-usual” or BAU conditions, but insteadpurposefully adopting higher-consumption operating conditions) prior toa period in which the customer anticipates participation in economicdemand response wholesale electricity markets; an artificially higherhistoric actual-use-based CBL, against which energy use reduction willbe measured, provides a potentially higher economic demand responserevenue. In this manner, the general goal of economic demand responseprograms to incentivize reduced electricity usage is undermined (by anartificially-increased electricity usage to establish a higher CBL).

Furthermore, the Inventors have recognized and appreciated that anhistorical actual-use-based CBL provides a long-term disincentive toparticipate in economic demand response wholesale electricity markets.In particular, as a given energy customer participates in economicdemand response wholesale electricity markets over time, their averageactual electricity use from retail suppliers is expected to decrease. Ifrevenue from such markets continues to be calculated with reference toan historical actual-use-based CBL, the potential for economic demandresponse revenue will decrease over time, as an economic settlementapproach based on historical actual-use CBL eventually will begin totreat incentivized electricity use reduction as “business-as-usual”operating conditions for the energy customer. This type of treatmentarguably will ultimately discourage participation in wholesaleelectricity markets. At very least, continued reliance on historicalactual-use-based CBL likely will compel an extension of a “look-back”period serving as a basis for determining CBL for energy customers whoactively participate in economic demand response wholesale electricitymarkets for significant periods of time. As longer look-back periods areadopted, the accuracy and relevance of historic actual-use-based CBLsfrom more distant time periods arguably will significantly decrease.

Accordingly, for at least the foregoing reasons, a simulated andpredictive CBL energy profile, based on a mathematical model of anenergy customer's energy asset(s) according to the concepts disclosedherein (rather than an historical actual-use-based CBL as conventionallyemployed), provides a significant improvement for more accuratelydetermining revenue earned from economic demand response wholesaleelectricity markets. In some examples, the mathematical model for theenergy asset(s) may not be predicated on any significantly historicalactual electricity use information for the energy asset(s), and insteadmay be based in part on physical attributes of the energy asset(s)themselves that relate to electricity use and/or electricity generation,as noted above. In this manner, simulated and predictive CBL energyprofiles based on such mathematical models are not substantivelyinfluenced by significantly historical actual electricity useinformation.

A self-tuning energy asset model according to a principle herein mayadapt itself to the current conditions of an energy asset. That is, thecomputation of the CBL calculations may reflect temporary changes orpermanent changes in the physical characteristics of an energy asset.The historical actual-use-based CBL may capture permanent changes in theenergy asset as well.

In other examples, the mathematical model for energy asset(s) may bepredicated on some degree of essentially real-time or near real-timefeedback (e.g., from one or more control systems actually controllingthe modeled energy asset(s)), which feedback may represent actualelectricity use. This feedback may be used, according to some examplesof the methods, apparatus and systems disclosed herein, to refine someaspects of the mathematical model; however, even when real-time or nearreal-time feedback representing actual electricity use is employed, insome examples the mathematical model is may be based on physicalattributes of the energy asset(s) themselves relating to electricity useand/or electricity generation.

Objective Cost Functions

In some examples, the mathematical model for the energy asset(s) isemployed to determine a suggested operating schedule over a given timeperiod T for the energy asset(s) (different than the BAU operatingschedule) based on a mathematical optimization of an “objective costfunction” representing the net energy-related cost to the energycustomer for operating the asset(s). In example implementations, theobjective cost function incorporates the mathematical model for theenergy asset(s) and specifies energy-related revenues from one or morewholesale energy markets (e.g., based on forecasted wholesale energyprices over the time period T for the one or more wholesale markets ofinterest), from which possible revenue may be available to the energycustomer. In some examples, the energy-related revenues specified in theobjective cost function may take into consideration a simulated customerbaseline (CBL) energy profile (discussed above) as a basis fordetermining such revenue.

The objective cost function employed in the mathematical optimization todetermine a suggested operating schedule for the energy asset(s) alsomay specify energy-related costs which are offset by the energy-relatedrevenues. In particular, in some examples, the energy-related costsincluded in the objective cost function may include “actual”energy-related costs (e.g., retail electricity costs, wholesaleelectricity costs representing revenue earned by the energy customer,fuel costs to run one or more electricity generation assets, operationand/or maintenance costs that may be associated with electricitygeneration and/or energy storage assets, lifetime and/or replacementcosts for electricity generation and/or energy storage assets,emissions-related costs, etc.). The energy-related costs included in theobjective cost function additionally or alternatively may include“indirect” energy-related costs, such as convenience/comfort costsassociated with the energy customer's adoption of a suggested operatingschedule different than the BAU operating schedule (theconvenience/comfort cost represents an “indirect” cost associated with achange in the customer's behavior with respect to operating itsasset(s), based on the incentive of possible energy-related revenue fromthe wholesale electricity markets).

Optimization of Objective Cost Function for Generating Energy AssetOperating Schedules

In one example, the objective cost function (which incorporates themathematical model of the energy asset(s)) may be provided to anoptimizer (a particularly-programmed processor, also referred to as a“solver”) that implements a mathematical optimization process todetermine a suggested operating schedule for the energy asset(s) over agiven time period T. In one conceptual illustration of the mathematicaloptimization process, some number N of candidate operating schedules aresuccessively applied to the mathematical model to generate simulatedenergy profiles corresponding to the candidate operating schedules. Anet energy-related cost represented by the objective cost function iscalculated for each simulated energy profile, and the candidateoperating schedule that minimizes the objective cost function (i.e.,minimizes the net energy-related cost) is selected as the suggestedoperating schedule. In some implementations, the amount of revenueavailable from the relevant wholesale electricity markets over the giventime period T is a significant factor dictating the candidate operatingschedule that is provided as an output of the optimizer. Theenergy-related costs may also include a reliability cost (such as basedon any voltage/VAR control activity in a microgrid application) and/oran emissions cost based on an emissions market.

Adopting Operating Schedules, Market Bids and Settlement

The suggested operating schedule in turn may be transmitted to theenergy customer (e.g., to an energy management system of the energycustomer), and the customer may choose to adopt or not adopt thesuggested operating schedule to actually operate its energy asset(s)over the particular time period T for which the optimization isperformed. In some implementations, a given operating schedule istransmitted to the energy customer in the form of one or more biassignals representing a change in an operating set point of one or moreassets, as a function of time over the time period T, from the typicalor “business-as-usual” (BAU) operating set point for the asset(s). Insome examples, the energy customer makes a choice to adopt a givensuggested operating schedule in tandem with making an offer (a “bid”) toprovide one or more wholesale electricity market products to theappropriate market pursuant to the adopted operating schedule.

If the energy customer adopts the suggested operating schedule toactually operate its energy asset(s) so as to provide a particularwholesale electricity market product pursuant to an accepted bid (e.g.,reduce its energy consumption), various information ultimately isobtained from the energy customer to facilitate a “settlement” processpursuant to which the customer is paid by the wholesale market operator(i.e., the RTO/ISO overseeing the wholesale electricity market(s) inwhich the customer is participating). For example, in one examplerelating to energy markets (wherein the “product” is energy usecurtailment), the energy customer's “metered load” (i.e., actual energyuse during the time period T in which the suggested operating scheduleis adopted) is measured, and compared to a simulated CBL based on themathematical model for the customer's energy asset(s). The energycustomer may then be paid for its economic demand response electricityuse reduction based on a difference between the simulated CBL and theactual metered load, multiplied by the actual wholesale energy priceduring the time period T for the market in question (e.g., LMP).

Apparatus, systems, methods and computer-readable media are describedfor determining an operating schedule of a controller of at least oneenergy storage asset operated by an energy customer of an electricitysupplier, so as to generate energy-related revenue, over a time periodT, associated with operation of the at least one energy storage assetaccording to the operating schedule, wherein the energy-related revenueavailable to the energy customer over the time period T is based atleast in part on a regulation market and/or a wholesale electricitymarket. The apparatus includes at least one communication interface, atleast one memory to store processor-executable instructions and amathematical model for the at least one energy storage asset, and atleast one processing unit. The mathematical model facilitates adetermination of the operating schedule for the controller of the atleast one energy storage asset based at least in part on an operationcharacteristic of the at least one energy storage asset, anenergy-generating capacity of a transport vehicle in communication withthe energy storage asset based on a motion of the transport vehicle, and(i) a regulation price associated with the regulation market and/or (ii)a forecast wholesale electricity price associated with the wholesaleelectricity market. The at least one processing unit is communicativelycoupled to the at least one communication interface and the at least onememory. Upon execution of the processor-executable instructions, the atleast one processing unit determines the operating schedule for thecontroller of the at least one energy storage asset using themathematical model, and controls the at least one communicationinterface to transmit to the energy customer the determined operatingschedule for the controller of the at least one energy storage asset,and/or controls the at least one memory so as to store the determinedoperating schedule for the controller.

In an example, the operating schedule for the controller of the at leastone energy storage asset can be determined based on an optimizationprocess using the mathematical model.

Non-limiting example operation characteristics of the at least oneenergy storage assets herein are a state of charge, a charge rate, adegree of non-linearity of charge rate a discharge rate, a degree ofnon-linearity of discharge rate, a round trip efficiency, or a degree oflife reduction.

Non-limiting examples of energy storage asset applicable to any of theapparatus, systems, and methods described herein include lithium ionbatteries, lead acid batteries, flow batteries, or dry cell batteries.

In an example, the motion of any of the transport vehicles herein can bea regenerative braking motion.

In an aspect, the energy-related revenue available to the energycustomer over the time period T is based at least in part on aregulation market and a wholesale electricity market. The operatingschedule for the controller of the at least one energy storage asset canbe generated to specify a first time interval within the time period Tfor use of the energy storage asset in the regulation market and asecond time interval within the time period T for use of the energystorage asset in the energy market.

In an aspect, upon execution of the processor-executable instructions,the at least one processing unit determines the operating schedule forthe controller of the at least one energy storage asset using themathematical model by minimizing a net energy-related cost over the timeperiod T. The net-energy related cost is based at least in part on anamount of energy accumulated in the energy storage asset based on theexpected energy-generating schedule of the transport vehicle,electricity generation by the at least one energy storage asset; andelectricity consumption by the at least one energy storage asset. Theenergy-related revenue available to the energy customer is based atleast in part on the minimized net energy-related cost.

In an aspect, the at least one energy storage asset can include at leastone wayside storage unit in electrical communication with the transportvehicle.

In an aspect, the at least one energy storage asset can include at leastone electric vehicle and/or at least one hybrid electric vehicle inelectrical communication with the at least one wayside storage unit.

In an aspect, the apparatus, systems, methods and computer-readablemedia can further include at least one energy generating asset inelectrical communication with the at least one energy storage asset,where the at least one energy storage asset includes at least onewayside storage unit in electrical communication with the transportvehicle, and where the wayside storage is configured to store an amountof energy generated by the at least one energy generating asset and/oran amount of energy generated based on the motion of the transportvehicle.

Non-limiting examples of applicable energy generating asset arephotovoltaic cells, wind generators, diesel generators, fuel cells, gasturbines, diesel generators, flywheels, electric vehicles, hybridelectric vehicles, or wind turbines.

An example system is also provided that includes any of the apparatusdescribed herein and a power control system. The power control systemcan include a controller. The power control system can be coupled to theat least one energy storage asset and to the apparatus so as to receivethe suggested operating schedule. The power control system can beimplemented to control operation of the at least one energy storageasset based at least in part on the operating schedule.

Apparatus, systems, methods and computer-readable media described hereincan be used for determining an operating schedule of a controller of atleast one energy storage asset operated by an energy customer of anelectricity supplier, so as to generate energy-related revenue, over atime period T, associated with operation of the at least one energystorage asset according to the operating schedule, wherein theenergy-related revenue available to the energy customer over the timeperiod T is based at least in part on a regulation market and awholesale electricity market that includes an energy market. Theapparatus includes at least one communication interface, at least onememory to store processor-executable instructions and a mathematicalmodel for the at least one energy storage asset, and at least oneprocessing unit. The mathematical model facilitates a determination ofthe operating schedule for the controller of the at least one energystorage asset based at least in part on an operation characteristic ofthe at least one energy storage asset, an energy-generating capacity ofa transport vehicle in communication with the energy storage asset basedon a motion of the transport vehicle, a forecast wholesale electricityprice associated with the energy market, and a regulation priceassociated with the regulation market. The at least one processing unitis configured to determine the operating schedule for the controller ofthe at least one energy storage asset using the mathematical model byminimizing a net energy-related cost over the time period T. Thenet-energy related cost is based at least in part on an amount of energyaccumulated in the energy storage asset based on the expectedenergy-generating schedule of the transport vehicle, the duration ofenergy storage asset participation in the regulation market, electricitygeneration by the at least one energy storage asset, and electricityconsumption by the at least one energy storage asset. The energy-relatedrevenue available to the energy customer is based at least in part onthe minimized net energy-related cost. The operating schedule specifies,during a time interval within the time period T, a first portion of anavailable output of the controller for use in the energy market and asecond portion of the available output of the controller for use for usein the regulation market. The at least one processing unit is alsoconfigured to control the at least one communication interface totransmit to the energy customer the operating schedule for thecontroller of the at least one energy storage asset and/or controls theat least one memory so as to store the determined operating schedule forthe controller

Apparatus, systems, methods and computer-readable media described hereincan be used for determining suggested operating schedule over a timeperiod T for at least one energy storage asset associated with atransportation operation implemented by an energy customer of a retailelectricity supplier, so as to reduce a net energy-related cost, overthe time period T, associated with electricity consumption and/orelectricity generation by the energy customer, wherein the netenergy-related cost is based at least in part on an energy-relatedrevenue available to the energy customer over the time period T from aregulation market and/or a wholesale electricity market. In thisexample, the apparatus includes at least one communication interface, atleast one processing unit, and at least one memory to storeprocessor-executable instructions, a mathematical model for the at leastone energy storage asset, and an objective cost function. Themathematical model specifies at least one function that calculates anenergy profile for the at least one energy storage asset based at leastin part on an operating schedule for the at least one energy storageasset applied to the mathematical model. The objective functionrepresents the net energy-related cost, the objective cost functionspecifying the energy-related revenue and at least one energy-relatedcost associated with operation of the at least one energy storage asset,the objective cost function calculating the net energy-related costbased at least in part on the energy profile calculated via themathematical model and (i) a regulation price associated with theregulation market and/or (ii) a forecast wholesale electricity priceassociated with the wholesale electricity market. The at least oneprocessing unit can be configured to determine the suggested operatingschedule, via an optimization process, as a solution that minimizes theobjective cost function, and control the at least one communicationinterface to transmit to the energy customer the suggested operatingschedule for the controller of the at least one energy storage assetand/or controls the at least one memory so as to store the determinedoperating schedule for the controller.

In an example, the at least one processing unit controls the at leastone communication interface to transmit the suggested operating scheduleto a power control system coupled to the at least one energy storageasset.

In an example, the power control system is coupled to the apparatus soas to receive the suggested operating schedule, where the power controlsystem controls operation of the at least one energy storage asset basedat least in part on the suggested operating schedule.

Apparatus, systems, methods and computer-readable media described hereincan be used for implementing a method of generating energy-relatedrevenue in connection with operation of an electric rail system, theelectric rail system including at least one energy storage asset tostore regenerative breaking energy arising from the operation of theelectric rail system. The method includes electronically determining asuggested operating schedule for charging and discharging of the atleast one energy storage asset based on optimization of an objectivecost function representing, at least in part, a demand response revenuefrom a regulation market, and controlling the at least one energystorage asset according to the suggested operating schedule.

In an example, a power control system is provided to control at leastone energy storage asset associated with an electric rail system, the atleast one energy storage asset configured to store regenerative breakingenergy arising from the operation of the electric rail system. The powercontrol system can include at least one communication interface toreceive a suggested operating schedule for charging and discharging ofthe at least one energy storage asset, the suggested operating schedulebeing based on optimization of an objective cost function representing,at least in part, a demand response revenue from a regulation market,and a controller, coupled to the at least one communication interface,to control the at least one energy storage asset according to thesuggested operating schedule received via the at least one communicationinterface.

Apparatus, methods and computer-readable media described herein can beused for determining an operating schedule of a controller of at leastone energy storage asset so as to generate energy-related revenue, overa time period T, associated with operation of the at least one energystorage asset according to the operating schedule. The energy-relatedrevenue available to the energy customer over the time period T is basedat least in part on a wholesale electricity market and a regulationmarket. In this example, the apparatus includes at least onecommunication interface, at least one memory to storeprocessor-executable instructions and a mathematical model for the atleast one energy storage asset, and at least one processing unit. Themathematical model facilitates a determination of the operating schedulefor the controller based at least in part on an operation characteristicof the at least one energy storage asset, an energy-generating capacityof a transport vehicle in communication with the energy storage assetbased on a motion of the transport vehicle, a regulation priceassociated with a regulation market, and a forecast wholesaleelectricity price associated with the wholesale electricity market. Theat least one processing unit is configured to determine the operatingschedule for the controller of the at least one energy storage assetbased on an optimization process using the mathematical model, whereinthe operating schedule specifies a first time interval within the timeperiod T for use of the energy storage asset in the regulation marketand a second time interval within the time period T for use of theenergy storage asset in the energy market. The at least one processingunit is also configured to control the at least one communicationinterface to transmit to the energy customer the operating schedule forthe controller of the at least one energy storage asset and/or controlsthe at least one memory so as to store the determined operating schedulefor the controller.

An example power control system including a controller can be coupled tothe at least one energy storage asset and to the apparatus so as toreceive the suggested operating schedule, where the power control systemcontrols operation of the at least one energy storage asset based atleast in part on the operating schedule.

Apparatus, methods and computer-readable media described herein can beused for determining an operating schedule of a controller of at leastone energy storage asset operated by an energy customer of anelectricity supplier, so as to generate energy-related revenue, over atime period T, associated with operation of the at least one energystorage asset according to the operating schedule, wherein theenergy-related revenue available to the energy customer over the timeperiod T is based at least in part on a regulation market and/or awholesale electricity market. The at least one energy storage assetincludes at least one electric vehicle and/or at least one hybridelectric vehicle. In this example, the apparatus includes at least onecommunication interface, at least one memory to storeprocessor-executable instructions and a mathematical model for the atleast one energy storage asset, and at least one processing unit. Themathematical model facilitates determination of the operating schedulefor the controller of the at least one energy storage asset based atleast in part on an operation characteristic of the at least one energystorage asset, an energy-generating capacity of at least one energygenerating asset in communication with the energy storage asset, and (i)a regulation price associated with the regulation market and/or (ii) aforecast wholesale electricity price associated with the wholesaleelectricity market. Upon execution of the processor-executableinstructions, the at least one processing unit determines the operatingschedule for the controller of the at least one energy storage assetusing the mathematical model, and controls the at least onecommunication interface to transmit to the energy customer thedetermined operating schedule for the controller of the at least oneenergy storage asset, and/or controls the at least one memory so as tostore the determined operating schedule for the controller.

In an example, the operating schedule for the controller of the at leastone energy storage asset can be determined based on an optimizationprocess using the mathematical model.

Non-limiting examples of applicable energy generating asset arephotovoltaic cells, wind generators, diesel generators, fuel cells, gasturbines, diesel generators, flywheels, electric vehicles, hybridelectric vehicles, or wind turbines.

In an aspect, upon execution of the processor-executable instructions,the at least one processing unit determines the operating schedule forthe controller of the at least one energy storage asset using themathematical model by minimizing a net energy-related cost over the timeperiod T. The net-energy related cost is based at least in part on anamount of energy accumulated in the energy storage asset based on theexpected energy-generating schedule of the transport vehicle,electricity generation by the at least one energy storage asset; andelectricity consumption by the at least one energy storage asset. Theenergy-related revenue available to the energy customer is based atleast in part on the minimized net energy-related cost.

In an example, the net energy-related cost is specified as a differencebetween an electricity supply cost and an economic demand responserevenue over the time period T.

In an example, the mathematical model facilitates a determination of theoperating schedule for the controller of the at least one energy storageasset based at least in part on a replacement cost for the at least oneenergy storage asset.

In an example, the operating schedule for the controller of the at leastone energy storage asset can specify a first time interval within thetime period T for use of the energy storage asset in the regulationmarket and a second time interval within the time period T for use ofthe energy storage asset in the wholesale electricity market.

In an example, the operating schedule for the controller of the at leastone energy storage asset can specify a first time interval within thetime period T for use of the energy storage asset in the regulationmarket and/or the wholesale electricity market, and a second timeinterval within the time period T for charging the at least one energystorage asset.

The following patent applications are hereby incorporated herein byreference in their entirety:

U.S. Provisional Application No. 61/477,067, filed on Apr. 19, 2011;

U.S. Provisional Application No. 61/552,982, filed on Oct. 28, 2011;

U.S. Non-provisional application Ser. No. 12/850,918, filed on Aug. 5,2010; and

U.S. Provisional Application No. 61/279,589, filed on Oct. 23, 2009.

The entire disclosure of these applications is incorporated herein byreference in its entirety, including drawings,

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings primarily are forillustrative purposes and are not intended to limit the scope of theinventive subject matter described herein. The drawings are notnecessarily to scale; in some instances, various aspects of theinventive subject matter disclosed herein may be shown exaggerated orenlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

FIG. 1 shows an example system that includes an energy storage asset, acontroller, and an energy generating asset, according to a principledescribed herein.

FIG. 2 shows an example apparatus according to a principle describedherein.

FIG. 3 shows an example system that includes an energy storage asset, acontroller, an energy generating asset, and an energy consuming asset,according to a principle described herein.

FIG. 4 shows an example system that includes an energy storage asset, acontroller, and an energy generating asset, according to a principledescribed herein.

FIG. 5 shows an example system that includes an energy storage asset anda controller, according to a principle described herein.

FIG. 6 illustrates an example block diagram representing an asset modelaccording to a principle described herein.

FIG. 7 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 8 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 9 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 10 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 11 shows an example energy storage asset optimization according toa principle described herein.

FIG. 12 shows an example generation schedule for an energy storageasset-energy generating asset co-optimization according to a principledescribed herein

FIG. 13 shows an example apparatus and components of a transportationsystem, according to a principle described herein.

FIG. 14 shows an example method for generating energy-related revenue,according to a principle described herein.

FIG. 15 shows example apparatus and components of an exampleimplementation of a transportation system, according to a principledescribed herein.

FIG. 16 shows a plot of an average weekday load versus time for atransportation system according to a principle described herein.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, inventive methods, apparatus, andsystems for determining a suggested operating schedule for energy assetsto facilitate revenue generation from wholesale electricity markets. Itshould be appreciated that various concepts introduced above anddiscussed in greater detail below may be implemented in any of numerousways, as the disclosed concepts are not limited to any particular mannerof implementation. Examples of specific implementations and applicationsare provided primarily for illustrative purposes.

As used herein, the term “includes” means includes but not limited to,the term “including” means including but not limited to. The term “basedon” means based at least in part on.

As used herein, the term “in communication with” includes directcommunication between elements as well as indirect communication betweenthe elements, such as by means of at least one intermediate component.As used herein, the term “in electrical communication with” includesdirect electrical communication between elements as well as indirectelectrical communication between the elements, such as by means of atleast one intermediate component.

As illustrated in the example of FIG. 1, the apparatus and methodsdescribed herein are applicable to a system 100 that includes at leastone energy storage asset 101, a controller 102 in communication with theenergy storage asset 101, and an energy generating asset 103. At leastone of the energy storage asset 101, the controller 102, and the energygenerating asset 103 is in communication with a power line 104. Thecontroller 102 facilitates charging of the energy storage asset 31 usingthe electricity supplied by power line 104 or feeding power generated bya discharge of the energy storage asset 101 to the power line 104. Theenergy generating asset 103 includes at least one regenerative energyasset. As also depicted in the non-limiting example of FIG. 1, thecontroller 102 facilitates charging of the energy storage asset 101using the regenerative energy that can be generated by an energygenerating asset 103 that is a regenerative energy generating asset.According to the principles herein, an apparatus 106 is provided thatcan be used to generate a suggested operating schedule for thecontroller of the energy storage asset 101. Operation of the controller102 and energy storage asset 101 according to the operating schedule canfacilitate an energy-related revenue to an energy customer. Thecontroller 102, the energy storage asset 101, and the apparatus 106 maybe located at one or more different facilities of the energy consumer.

In an example, the apparatus 106 can be used according to the principlesherein to determine the suggested operating schedule for the controller102 of the at least one energy storage asset 102 based at least in parton a mathematical model applied to an operation characteristic of the atleast one energy storage asset, an energy-generating capacity of atransport vehicle in communication with the energy storage asset basedon a motion of the transport vehicle, and a forecast wholesaleelectricity price associated with an energy market and/or a regulationprice associated with the regulation market. An energy generating asset103 that is a regenerative energy generating asset includes thetransport vehicle, and the regenerative braking energy is generated froma braking motion of the transport vehicle. The operating schedule forthe controller of the at least one energy storage asset 101 is generatedbased on an optimization process using the mathematical model.

As used herein, the term “transport vehicle” refers to at least onerailroad car of a train of a transportation system or at least oneplatform or compartment of a carriage system. The at least one railroadcar can be a part of an electric railway power traction system. Acarriage system includes an elevator system or any other similar systembased on a platform or compartment housed in a shaft for transportingpeople or things to different vertical levels or horizontal positions.

The expected energy-generating schedule of the energy generating asset103 can be determined based on its expected or averaged energygenerating capacity. For example, where energy generating asset 103 is aregenerative braking energy of a regenerative energy generating asset,the expected energy-generating schedule can be determined based on theset schedule of trains in a transportation system or the averaged orprojected motion of transport vehicles in a transportation system or acarriage system.

In an example, the at least one energy storage asset 101 is at least onewayside energy storage asset. Non-limiting examples of the waysideenergy storage assets are energy storage assets located at thesubstations of transportation systems, or energy storage assets locatedat the power units for the carriage systems.

In another example, the energy generating asset 103 can include at leastone electric vehicle and/or at least one hybrid electric vehicle.

Non-limiting examples of energy storage assets include batteries, iceunits, and compressed air. Non-limiting examples of batteries includelithium ion batteries, lead-acid batteries, flow batteries, or dry celltechnology batteries.

In the non-limiting example of FIG. 1, the controller 102 facilitatesthe communication between the regenerative energy generating asset 103and the energy storage asset 101. In another example, the regenerativeenergy generating asset 103 may communicate with the energy storageasset 101 via one or more other components including the controller 102.

The apparatus and methods herein facilitate generation of energy-relatedrevenue for an energy customer of an electricity supplier, where theenergy customer commits an amount of energy from the at least one energystorage asset to a regulation market and/or an energy market. In anexample, the electricity supplier may be a retail electricity supplierthat supplies the electricity to the energy customer at a retail price.In another example, the electricity supplier may supply the electricityto the energy customer at a contracted for or negotiated price. Invarious examples herein, the energy customer may allow an amount ofcapacity of the energy storage asset to be committed to the energymarket or the charge/discharge capacity of the energy storage asset tobe committed to the regulation market. When implemented, the apparatusand methods described herein may allow the energy customer to generatean amount of energy-related revenue over a time period that an amount ofcharge capacity and/or charge/discharge capacity of the energy storageasset is committed to the regulation market and/or the energy market.

In a non-limiting example, an apparatus or a method described herein canbe used to generate an operating schedule for a controller thatcommunicates with the energy storage asset. The controller is capable ofexercising an amount of control over the rate of charging or energygeneration of the energy storage asset. As a result, the controller canbe used to maintain the state of charge of the energy storage asset, orchange its state of charge controllably. Operation of the controller,and hence the energy storage asset, according to the operating schedulegenerated by an apparatus or a method herein over the time period maymake available to the energy customer an amount of energy-relatedrevenue based at least in part on the wholesale electricity market.

A non-limiting example of the apparatus 200 according to the principlesdescribed herein is illustrated in FIG. 2. The apparatus 200 includes atleast one communication interface 211, at least one memory 212, and atleast one processing unit 213. The at least one processing unit 213 iscommunicatively coupled to the at least one communication interface 211and the at least one memory 212.

The at least one memory 212 is configured to store processor-executableinstructions 214 and a mathematical model 215 for the at least oneenergy storage asset. As described in greater detail below, themathematical model determines the operating schedule for the controllerbased on data 216 associated with parameters, including but not limitedto, an operation characteristic of the energy storage asset, anoperation characteristic of a regenerative energy generating asset incommunication with the energy storage asset, and a regulation priceassociated with a regulation market and/or a forecast wholesaleelectricity price associated with the wholesale electricity market.

In a non-limiting example, the at least one processing unit 213 executesthe processor-executable instructions 214 stored in the memory 212 atleast to determine the operating schedule for the controller of theenergy storage asset using the mathematical model 215. The at least oneprocessing unit 213 also executes processor-executable instructions 214to control the communication interface 211 to transmit to the energycustomer 217 the operating schedule that has been determined for thecontroller and/or controls the memory 212 to store the determinedoperating schedule for the controller. In a non-limiting example, theprocessing unit 213 may execute processor-executable instructions 214 tocontrol the communication interface 211 to transmit to the operatingschedule directly to the controller.

The operation characteristic of the energy storage asset may be itsstate of charge, charge rate, the degree of non-linearity of the chargerate, discharge rate, degree of non-linearity of the discharge rate,round trip efficiency, and degree of life reduction. In an example wherethe operation characteristic of the energy storage asset is its chargerate and/or discharge rate, the operating schedule for the controllermay include suggested different time intervals for charging the energystorage asset or discharging the energy storage asset during the timeperiod T that the system is in operation. As a non-limiting example, theoperating schedule for the controller may indicate a time interval forcharging the energy storage asset that coincides with a correspondingtime interval during which the forecast wholesale electricity pricefalls below a predetermined threshold value. As another non-limitingexample, the operating schedule for the controller may indicate a timeinterval of discharging the energy storage asset that coincides with acorresponding time interval during which the forecast wholesaleelectricity price exceeds a predetermined threshold value.

The apparatus and methods described herein are also applicable to asystem as depicted in the example of FIG. 3. In this example, theapparatus includes at least one energy storage asset 301, a controller302 in communication with the at least one energy storage asset 301, atleast one energy generating asset 303 and at least one energy consumingasset 304. The at least one energy storage asset 301 is in communicationwith a power line 305. The energy generating asset 303 can be aregenerative energy generating asset. The energy generating asset 303can include at least one other energy generating asset, includingphotovoltaic cells, fuel cells, gas turbines, diesel generators,flywheels, electric vehicles, hybrid electric vehicles, and windturbines. The controller 302 in communication with the energy storageasset 301 facilitates charging of the energy storage asset 301 using theelectricity supplied by power line 305. The controller 302 alsofacilitates feeding power generated by a discharge of the energy storageasset 301 to the power line 305. As depicted in the non-limiting exampleof FIG. 1, the controller 302, the energy storage asset 301, the energygenerating asset 303, and the energy consuming asset 304 may be locatedbehind a power meter 305. For example, all of the controller 302, theenergy storage asset 301, the energy generating asset 303, and theenergy consuming asset 304 may be located at one or more facilities ofthe energy consumer.

In the non-limiting example of FIG. 3, the controller 302 facilitatesthe communication between the energy consuming asset, the energy storageasset, and the energy generating asset. In other examples, the energyconsuming asset may communicate with the energy storage asset via one ormore other components including the controller 302.

An apparatus according to the principles of FIG. 2 may be implementedrelative to the system of FIG. 3 to generate an operating schedule forthe controller 302. In this example, the mathematical model facilitatesdetermination of the operating schedule for the controller of the atleast one energy storage asset further based at least in part on anexpected energy-generating schedule of the energy generating asset incommunication with the energy storage asset and the energy consumingasset. Any principles and/or implementations described herein, includingabove, in connection with FIG. 1 are also applicable to the system ofFIG. 3.

The operation characteristic of the energy consuming asset may be itsload use schedule. For example, the operation characteristic of theenergy consuming asset can be its energy consumption profile as afunction of time. The energy consuming asset may be a controllable assetor a fixed-load asset. A fixed-load asset is an energy consuming assetwhose energy consumption characteristics may not be readily modified,even if it varies over time. The energy consumption characteristics of acontrollable energy consuming asset may be modified by changingparameters of operation of the system. A non-limiting example of anoperation characteristic for a controllable energy consuming asset isits set point. The set point may be a controllable set point, e.g., itmay be controllable as a function of time or temperature. For example,where the controllable energy consuming asset is a transport vehiclewith a variable internal temperature controlled by a heating,ventilation and air conditioning (HVAC) system, the operationcharacteristic may be a temperature set point for the HVAC system. Inanother example, the transport vehicle also can serve as an energyconsuming asset to the extent that some of the regenerative brakingenergy that is used to charge the energy storage asset is used toaccelerate (i.e., push) the transport vehicle from the station.

As described herein, in an example, an amount of energy of the energystorage asset may be generated and supplied to the power line at adischarge rate to generate energy-related revenue for the energycustomer in an energy market. The energy-related revenue can depend on aforecast wholesale electricity price associated with the wholesaleelectricity market, and may be determined based on computation of anet-energy related cost. The net energy related cost may be computedbased on the supply costs for supplying electricity to the customer anda demand response revenue. An apparatus and method herein can beimplemented to generate an operating schedule for the controller of theenergy storage asset that provides recommendations for the timing ofcharging and discharging of the energy storage asset.

In an example, the processing unit can be configured to determine theoperating schedule for the controller of the at least one energy storageasset using the mathematical model by minimizing a net energy-relatedcost over the relevant time period (T). The net energy-related cost canbe associated with electricity generation by the energy storage asset,electricity consumption by the energy storage asset, and electricityconsumption by the energy consuming asset. Here, the energy-relatedrevenue available to the energy customer may be computed based at leastin part on the minimized net energy-related cost.

The net energy-related cost may be specified as a difference between theelectricity supply cost and the economic demand response revenue overthe pertinent time period.

In an example, the processing unit can be configured to determine theoperating schedule for the controller using the mathematical model and arepresentative customer baseline (CBL) energy profile for the energyconsuming asset over the time period (T). As used herein, the term“representative customer baseline energy profile” or “representative CBLenergy profile” encompasses representations of the energy customer'senergy usage in the absence of change of behavior according to theprinciples described herein. As non-limiting examples, the“representative customer baseline energy profile” or “representative CBLenergy profile” includes an estimation based on the energy customer'sbusiness-as-usual (BAU) operations, including any form of averaged orweighted measure based on measures of historical BAU operations. Herein,the representative CBL energy profile represents a typical operation ofthe at least one energy consuming asset by the energy customer. Forexample, where the energy consuming asset is a fixed-load asset, therepresentative CBL may be determined as the energy consumption profilefor the energy consuming asset.

Where the operating schedule for the controller is generated based onusing the mathematical model and a representative customer baseline(CBL) energy profile, the economic demand response revenue may becomputed based on the forecast wholesale electricity price, theelectricity generation by the energy storage asset, the electricityconsumption by the energy storage asset, and the representative CBLenergy profile for the energy consuming asset.

In an example herein, a portion of the energy of the energy storageasset may be committed to the regulation market. That is, the wholesaleelectricity market for the energy customer would include an energymarket and a regulation market. In an example where the forecastwholesale electricity price is for the energy market, the operatingschedule for the controller may specify optimal time intervals for useof the energy storage asset in the regulation market. For example, ifthe forecast wholesale electricity price for the energy market isprojected to fall below a predetermined threshold value during a timeinterval, the operating schedule for the controller may recommend of theenergy storage asset in the regulation market during that time interval.Where the forecast wholesale electricity price for the energy market isprojected to fall below a predetermined threshold value during a timeinterval, the operating schedule for the controller may recommend use ofthe energy storage asset in the regulation market during that timeinterval.

According to an example of the principles herein, the wholesaleelectricity market may include both the energy market and the regulationmarket, and the operating schedule generated may facilitateimplementation of the energy storage asset in both the energy market andthe regulation market. According to a principle of virtual partitioningdescribed herein, the operating schedule for the controller may beconfigured so that the energy customer may participate in both theenergy market and the regulation market concurrently the energy storageasset. In a non-limiting example, the operating schedule for thecontroller of the energy storage asset may specify that, during a giventime interval, a first portion of an available state of charge (SOC) ofthe energy storage asset may be used in the energy market and a secondportion of the available SOC of the energy storage asset may becommitted to the regulation market. The operating schedule generate forthe controller may be used to energy-related revenue for the energyconsumer based on both the energy market and the regulation market. Theprinciples and implementations described above in connection to FIG. 1are also applicable to a system operating according to the principles ofvirtual partitioning.

The apparatus 200 illustrated in FIG. 2 may be used to implement thevirtual partitioning described herein. In this non-limiting example, theat least one memory 12 is configured to store processor-executableinstructions 14 and a mathematical model 15 for the at least one energystorage asset, where the mathematical model determines the operatingschedule for the controller based on data 16 associated with parameters,including but not limited to, an operation characteristic of the energystorage asset, an operation characteristic of the energy consuming assetin communication with the at least one energy storage asset, and aforecast wholesale electricity price associated with the wholesaleelectricity market.

In this non-limiting example of virtual partitioning, the at least oneprocessing unit 213 executes the processor-executable instructions 214stored in the memory 212 at least to determine the operating schedulefor the controller of the energy storage asset using the mathematicalmodel 215, where the operating schedule specifies, during a timeinterval less than time period T, a proportion of an available state ofcharge (SOC) of the energy storage asset for use in the energy marketand a remaining proportion of the available SOC of the energy storageasset for use in the regulation market. The at least one processing unit213 also executes processor-executable instructions 14 to control thecommunication interface 11 to transmit to the energy customer 217 theoperating schedule that has been determined for the controller and/orcontrols the memory 212 to store the determined operating schedule forthe controller. In a non-limiting example, the processing unit 213 mayexecute processor-executable instructions 214 to control thecommunication interface 211 to transmit to the operating scheduledirectly to the controller.

In a non-limiting example, the operation characteristic of the at leastone energy storage asset can be at least one of a state of charge, acharge rate, a degree of non-linearity of charge rate a discharge rate,a degree of non-linearity of discharge rate, a round trip efficiency,and a degree of life reduction. The proportion of the available SOC ofthe energy storage asset for use in the energy market may be supplied asa direct-current (DC) signal, while the remaining proportion of theavailable SOC of the energy storage asset for use in the regulationmarket may be delivered at a variable charge rate or variable dischargerate.

In an example where the energy storage asset is used in both the energymarket and the regulation market, constraints may be placed on the totalamount of energy used. For example, the total SOC of the energy storageasset over the time that it is used in both markets can be constrainedto be depleted to no less than a minimum allowed SOC value or charged tono more than a maximal allowed SOC value. In an example, the sum of theproportion of the available SOC of the at least one energy storage assetfor use in the energy market and the remaining proportion of theavailable SOC of the at least one energy storage asset for use in theregulation market can be constrained to be no less than a minimalallowed SOC and no more than a maximal allowed SOC. As a non-limitingexample, the maximal allowed SOC of the energy storage asset may be setat 80%, and the minimal allowed SOC may be set at 20%.

The apparatus and methods described herein are also applicable to asystem as depicted in the example of FIG. 4. In this example, theapparatus includes an energy storage asset 401, a controller 402 incommunication with the energy storage asset 401, and an energygenerating asset 403 in communication with a power line 404. Thecontroller 402 facilitates charging of the energy storage asset 31 usingthe electricity supplied by power line 404. The controller 402 alsofacilitates feeding power generated by a discharge of the energy storageasset 401 to the power line 404. Non-limiting examples of energygenerating assets include photovoltaic cells, fuel cells, gas turbines,diesel generators, flywheels, electric vehicles, and wind turbines. Asdepicted in the non-limiting example of FIG. 4, the controller 402, theenergy storage asset 401, and the energy generating asset 403 may belocated behind a power meter 405. For example, all of the controller402, the energy storage asset 401, and the energy generating asset 33may be located at one or more facilities of the energy consumer.

As a non-limiting example, the system of FIG. 4 can apply to an exampleimplementation where an aggregation of electric vehicles and/or hybridelectric vehicles that are in communication with at least one energygenerating asset are committed to a regulation market and/or an energymarket as energy storage assets to facilitate deriving energy-relatedrevenue.

As another non-limiting example, the system of FIG. 4 can apply to anexample implementation where wayside energy storage asset(s) are coupledwith the aggregation of electric vehicles and/or hybrid electricvehicles, and the combined capacity is committed to the regulationmarket and/or the energy market as energy storage assets to facilitatederiving the energy-related revenue.

In the non-limiting example of FIG. 4, the controller 402 facilitatesthe communication between the energy storage asset and the energygenerating asset. In other examples, the energy consuming asset maycommunicate with the energy storage asset via one or more othercomponents including the controller 402.

An apparatus according to the principles of FIG. 2 may be implementedrelative to the system of FIG. 4 to generate an operating schedule forthe controller 402. In this example, the mathematical model facilitatesdetermination of the operating schedule for the controller of the atleast one energy storage asset further based at least in part on anexpected energy-generating schedule of the energy generating asset incommunication with the energy storage asset. Any principles and/orimplementations described herein, including above, in connection withFIG. 1 are also applicable to the system of FIG. 4.

In a non-limiting example, the apparatus of FIG. 2 can be used fordetermining an operating schedule of a controller of at least one energystorage asset operated by an energy customer of an electricity supplier,so as to generate energy-related revenue, over a time period T,associated with operation of the at least one energy storage assetaccording to the operating schedule, wherein the energy-related revenueavailable to the energy customer over the time period T is based atleast in part on a wholesale electricity market. In this example, theapparatus includes at least one communication interface, at least onememory to store processor-executable instructions and a mathematicalmodel for the at least one energy storage asset, and at least oneprocessing unit. The mathematical model facilitates determination of theoperating schedule for the controller based at least in part on anoperation characteristic of the at least one energy storage asset, anexpected energy-generating schedule of the energy generating asset incommunication with the energy storage asset, and a forecast wholesaleelectricity price associated with the wholesale electricity market. Theat least one processing unit can be configured to determine theoperating schedule for the controller of the at least one energy storageasset using the mathematical model, and control the at least onecommunication interface to transmit to the energy customer the operatingschedule for the controller of the at least one energy storage assetand/or controls the at least one memory so as to store the determinedoperating schedule for the controller.

In this example, the at least one processing unit can be configured todetermine the operating schedule for the controller of the at least oneenergy storage asset using the mathematical model by minimizing a netenergy-related cost over the time period T. The net-energy related costis based at least in part on the amount of energy generation by the atleast one energy generating asset, electricity generation by the atleast one energy storage asset; and electricity consumption by the atleast one energy storage asset. The energy-related revenue available tothe energy customer is based at least in part on the minimized netenergy-related cost. The net energy-related cost may be specified as adifference between an electricity supply cost and an economic demandresponse revenue over the time period T. The energy generating asset maybe a photovoltaic cell, a fuel cell, a gas turbine, a diesel generator,a flywheel, an electric vehicle, or a wind turbine. The operationcharacteristic of the at least one energy storage asset may be a stateof charge, a charge rate, a degree of non-linearity of charge rate adischarge rate, a degree of non-linearity of discharge rate, a roundtrip efficiency, or a degree of life reduction.

In another non-limiting example, the apparatus of FIG. 2 can be used fordetermining an operating schedule of a controller of at least one energystorage asset operated by an energy customer of an electricity supplier,so as to generate energy-related revenue, over a time period T,associated with operation of the at least one energy storage assetaccording to the operating schedule. The energy-related revenueavailable to the energy customer over the time period T is based atleast in part on a wholesale electricity market, and the wholesaleelectricity market includes an energy market and a regulation market. Inthis example, the apparatus includes at least one communicationinterface, at least one memory to store processor-executableinstructions and a mathematical model for the at least one energystorage asset, and at least one processing unit. The mathematical modelfacilitates a determination of the operating schedule for the controllerbased at least in part on an operation characteristic of the at leastone energy storage asset, an expected energy-generating schedule of anenergy generating asset in communication with the energy storage asset,a forecast wholesale electricity price associated with the energymarket, and a regulation price associated with the regulation market.The at least one processing unit is configured to determine theoperating schedule for the controller of the at least one energy storageasset using the mathematical model by minimizing a net energy-relatedcost over the time period T. The net-energy related cost is based atleast in part on the amount of energy generation by the at least oneenergy generating asset, duration of energy storage asset participationin the regulation market, electricity generation by the at least oneenergy storage asset and electricity consumption by the at least oneenergy storage asset. The energy-related revenue available to the energycustomer is based at least in part on the minimized net energy-relatedcost. The operating schedule specifies, during a time interval withinthe time period T, a first portion of an available output of thecontroller for use in the energy market and a second portion of theavailable output of the controller for use for use in the regulationmarket. The at least one processing unit is also configured to controlthe at least one communication interface to transmit to the energycustomer the operating schedule for the controller of the at least oneenergy storage asset and/or controls the at least one memory so as tostore the determined operating schedule for the controller.

In this example, the available output of the controller may be a chargerate of the at least one energy storage asset or a discharge rate of theat least one energy storage asset. The net energy-related cost isspecified as a difference between an electricity supply cost and aneconomic demand response revenue over the time period T. The operationcharacteristic of the at least one energy storage asset may be a stateof charge, a charge rate, a degree of non-linearity of charge rate adischarge rate, a degree of non-linearity of discharge rate, a roundtrip efficiency, and a degree of life reduction.

The apparatus and methods described herein are also applicable to asystem as depicted in the example of FIG. 5. In this example, theapparatus includes an energy storage asset 501, and a controller 502 incommunication with the energy storage asset 501 and in communicationwith a power line 504. The controller 502 facilitates charging of theenergy storage asset 31 using the electricity supplied by power line504. The controller 502 also facilitates feeding power generated by adischarge of the energy storage asset 501 to the power line 504.Non-limiting examples of energy generating assets include photovoltaiccells, fuel cells, gas turbines, diesel generators, flywheels, electricvehicles, and wind turbines. As depicted in the non-limiting example ofFIG. 5, the controller 502, and the energy storage asset 501 may belocated behind a power meter 503. For example, the controller 502 andthe energy storage asset 501 may be located at one or more facilities ofthe energy consumer.

As a non-limiting example, the system of FIG. 5 can apply to an exampleimplementation where an aggregation of electric vehicles and/or hybridelectric vehicles that are in communication with at least one energygenerating asset are committed to a regulation market and/or an energymarket as energy storage assets to facilitate deriving energy-relatedrevenue.

As another non-limiting example, the system of FIG. 5 can apply to anexample implementation where wayside energy storage asset(s) are coupledwith the aggregation of electric vehicles and/or hybrid electricvehicles, and the combined capacity is committed to the regulationmarket and/or the energy market as energy storage assets to facilitatederiving the energy-related revenue.

In the non-limiting example of FIG. 5, the controller 502 facilitatesthe communication between the energy storage asset and the energygenerating asset. In other examples, the energy consuming asset maycommunicate with the energy storage asset via one or more othercomponents including the controller 502.

An apparatus according to the principles of FIG. 2 may be implementedrelative to the system of FIG. 5 to generate an operating schedule forthe controller 502. In this example, the mathematical model facilitatesdetermination of the operating schedule for the controller of the atleast one energy storage asset further based at least in part on anexpected energy-generating schedule of the energy generating asset incommunication with the energy storage asset. Any principles and/orimplementations described herein, including above, in connection withFIG. 1 are also applicable to the system of FIG. 5.

In another non-limiting example, the apparatus of FIG. 2 can be used fordetermining an operating schedule of a controller of at least one energystorage asset operated by an energy customer of an electricity supplier,so as to generate energy-related revenue, over a time period T,associated with operation of the at least one energy storage assetaccording to the operating schedule, wherein the energy-related revenueavailable to the energy customer over the time period T is based atleast in part on a wholesale electricity market, and wherein thewholesale electricity market includes an energy market and a regulationmarket. The apparatus includes at least one communication interface, atleast one memory to store processor-executable instructions and amathematical model for the at least one energy storage asset, and atleast one processing unit. The mathematical model facilitates adetermination of the operating schedule for the controller of the atleast one energy storage asset based at least in part on an operationcharacteristic of the at least one energy storage asset, a forecastwholesale electricity price associated with the energy market, and aregulation price associated with the regulation market. The at least oneprocessing unit is configured to determine the operating schedule forthe controller of the at least one energy storage asset using themathematical model by minimizing a net energy-related cost over the timeperiod T. The net-energy related cost is based at least in part on theduration of energy storage asset participation in the regulation market,electricity generation by the at least one energy storage asset, andelectricity consumption by the at least one energy storage asset. Theenergy-related revenue available to the energy customer is based atleast in part on the minimized net energy-related cost. The operatingschedule specifies, during a time interval within the time period T, afirst portion of an available output of the controller for use in theenergy market and a second portion of the available output of thecontroller for use for use in the regulation market. The at least oneprocessing unit is also configured to control the at least onecommunication interface to transmit to the energy customer the operatingschedule for the controller of the at least one energy storage assetand/or controls the at least one memory so as to store the determinedoperating schedule for the controller.

In this example, the available output of the controller is a charge rateof the at least one energy storage asset or a discharge rate of the atleast one energy storage asset. The net energy-related cost may bespecified as a difference between an electricity supply cost and aneconomic demand response revenue over the time period T. The operationcharacteristic of the at least one energy storage asset is a state ofcharge, a charge rate, a degree of non-linearity of charge rate adischarge rate, a degree of non-linearity of discharge rate, a roundtrip efficiency, or a degree of life reduction.

Energy Asset Modeling

To facilitate the mathematical optimization process for generating asuggested operating schedule for one or more energy assets according tovarious examples of the principles herein, a mathematical modelrepresenting an energy customer's energy asset(s) is formulated andemployed to simulate an “energy profile” for the asset(s). In oneaspect, the model is essentially specified by one or more mathematicalfunctions that at least in part represent physical attributes of theenergy asset(s) themselves as they relate to electricity use and/orelectricity generation. Depending on the energy asset(s) operated by theenergy customer, the mathematical function(s) defining an asset modelmay represent a single energy asset or an aggregation of multiple energyassets operated by the customer. For purposes of the discussion herein,the term “asset model,” unless otherwise qualified, is used generally todenote a model representing either a single energy asset or anaggregation of multiple energy assets.

To illustrate the general concept of an asset model, a model is firstconsidered for one or more energy assets that not only may be turned“on” or “off,” but that may be controlled at various “operating setpoints.” For example, consider the case of a transport vehicle thatincludes a heating, ventilation and air conditioning (HVAC) system fortemperature control, for which the customer may choose differenttemperature set points at different times (e.g., thermostat settings);accordingly, in this example, the temperature set points constitute“operating set points” of the transport vehicle. In this example, themagnitude of the operating set point may vary as a function of time t,in a continuous or step-wise manner (e.g., Temp(t)=72 degrees F. for 9PM<t<9 AM; Temp(t)=68 degrees F. for 9 AM<t<9 PM). In other examples ofenergy assets that merely may be turned “on” or “off,” the magnitude ofthe operating set point may be binary (i.e., on or off), but therespective on and off states may vary as a function of time t (e.g.,over a given time period T).

Based on the notion of time-varying operating set points for energyassets, the term “operating schedule” as used herein refers to anoperating set point of one or more energy assets as a function of time,and is denoted by the notation SP(t):

-   -   SP(t)≡operating schedule for one or more energy assets.

The amount of energy used (and/or generated) by a particular asset orgroup of assets in a given time period T is referred to herein as an“energy profile.” In various implementations discussed herein, theenergy profile of one or more assets often depends at least in part on agiven operating schedule SP(t) for the asset(s) during the time periodT. For a fixed-load asset, the energy profile may not depend on a givenoperating schedule SP(t). Accordingly, an energy asset model specifiesone or more mathematical functions for calculating an energy profile(i.e., electricity use and/or electricity generation as a function oftime) for the asset(s), based on a proposed operating schedule for theasset(s) applied as an input to the model. The one or more functionsconstituting the asset model are denoted herein generally as F (and forsimplicity the term “function” when referring to F may be used in thesingular), and the model may be conceptually represented usingmathematical notation as:

F(SP(t))=EP(t),   Eq. 1

where the operating schedule SP(t) is an argument of the function F, andthe energy profile of the modeled asset(s) as a function of time isdenoted as EP(t). In a non-limiting example, EP(t) has units of MWh.FIG. 6 illustrates a simple block diagram representing the asset modelgiven by Eq. 1.

In various examples, the function(s) F defining a particular asset modelmay be relatively simple or arbitrarily complex functions of theargument SP(t) (e.g., the function(s) may involve one or more constants,have multiple terms with respective coefficients, include terms ofdifferent orders, include differential equations, etc.) to reflect howthe asset(s) consume or generate energy in response to the operatingschedule SP(t). In general, the particular form of a given function F,and/or the coefficients for different terms, may be based at least inpart on one or more physical attributes of the asset(s), and/or theenvironment in which the asset(s) is/are operated, which may impact theenergy profile of the asset(s) pursuant to the operating schedule. Morespecifically, depending on the type of energy asset(s) being modeled,the mathematical model may be formulated to accept other inputs (inaddition to the operating schedule SP(t)), and/or to accommodatevariable parameters of a given function F (e.g., via time-dependentcoefficients of different terms of the function), to facilitatecalculation of the energy profile EP(t) based on a proposed operatingschedule SP(t).

For example, in the case of the transport vehicle discussed above,and/or other assets for which thermodynamic considerations arepertinent, various internal factors that may impact the asset's energyprofile in general (e.g., occupancy; a presence of equipment such ascomputers and other instrumentation that may affect heating or coolingin an environment; thermal inertia due to insulation, vehicle materials,windows; etc.) may be considered in the formulation of the form of thefunction F itself, and/or coefficients for different terms of thefunction F. In some examples discussed in further detail below, thefunction F may be dynamically adjusted based on observing actual energyusage over time by the asset(s) pursuant to control via a particularoperating schedule (i.e., coefficients of function terms initially maybe estimated, and subsequently adjusted over time based on real-timefeedback from controlled assets).

Similarly, the mathematical model for the asset(s) may be configured toconsider as an input to the model actual or forecast ambientenvironmental conditions (e.g., temperature, humidity, ambientlight/cloud cover, etc.) as a function of time, collectively denoted as“weather information” W(t), which may impact the energy profile of oneor more assets. In this case, the model may be conceptually representedas:

F(SP(t),W(t))=EP(t),   Eq. 2

where both the operating schedule SP(t) and the weather information W(t)are arguments of the function F. FIG. 7 illustrates a simple blockdiagram representing the asset model given by Eq. 2. It should beappreciated that, while weather information W(t) is noted above asproviding another possible input to the model in addition to theoperating schedule SP(t), in other examples one or more other inputs tothe model may be provided and considered as arguments to the function F(and accordingly taken into consideration in the function) for purposesof calculating an energy profile EP(t) for the asset(s).

In another example herein, the mathematical model for a system thatincludes a controllable asset, such as an energy storage asset and anassociated controller, may be configured to consider as an input to themodel the control vector for the controller as a function of time,denoted as u(t), which may impact the energy profile. In this case, themodel may be conceptually represented as:

F(u(t))=EP(t),   Eq. 3

where both the control vector of the controller is an argument of thefunction F. FIG. 8 illustrates a simple block diagram representing theasset model given by Eq. 3. It should be appreciated that, while thecontrol vector u(t) is noted above as providing input to the model, inother examples, one or more other inputs to the model may be providedand considered as arguments to the function F (and accordingly takeninto consideration in the function) for purposes of calculating anenergy profile EP(t) for the asset(s). An energy storage asset hereingenerally refers to an asset that can store a form of energy and releaseit as usable energy (or power) over time. Non-limiting examples ofenergy storage assets include batteries, ice units, compressed air,flywheel, heated liquids, and heated solids. Non-limiting examples ofbatteries include lithium ion batteries, lead-acid batteries, flowbatteries, or dry cell technology batteries.

In yet another example herein, the mathematical model for a system thatincludes an energy consuming asset, such as but not limited to atransport vehicle, and a controllable asset, such as but not limited toan energy storage asset and an associated controller, may be configuredto consider as an input to the model the control vector for thecontroller as a function of time, denoted as u(t), and temperaturedependent operating set points for the energy consuming asset (itsoperating schedule). In this case, the model may be conceptuallyrepresented as:

F(u(t),SP(t))=EP(t),   Eq. 4

where both the control vector of the controller is an argument of thefunction F. FIG. 9 illustrates a simple block diagram representing theasset model given by Eq. 4. The control vector for a controller,u(t)=C_(t)+D_(t), may be expressed as:

C _(t) =u _(1,t) *C/D _(max)

D _(t) =u _(2,t) *C/D _(max)   Eq. 5

with the constraints that u_(1,t)*u_(2,t)=0 and 0<u_(1,t), u_(2,t)<1,where represents C/D_(max) the maximum charge rate or discharge ratecapacity of the controller in communication with the energy storageasset.

In yet another example herein, the mathematical model for a system thatincludes an energy consuming asset, such as but not limited to atransport vehicle, and a controllable asset, such as but not limited toan energy storage asset and an associated controller, may be configuredto consider as an input to the model the control vector for thecontroller as a function of time, denoted as u(t), and temperaturedependent operating set points for the energy consuming asset (itsoperating schedule). FIG. 10 illustrates a simple block diagramrepresenting the asset model for such as system according to theprinciples herein. In this case, the model may have outputs of the stateof charge (SOC) of the energy storage asset as a function of time t, thereturn-a-temperature (RAT) as a function of time t (for, e.g., a HVAC orother similar equipment), and the energy profile of the energy consumingasset (e.g., the transport vehicle). Other inputs to the system can beweather information (W(t)) and/or feedback from other energy assets inthe system (V). This model can be used, e.g., for co-optimization of anenergy storage asset and an energy consuming asset for the energymarket.

In an example according to a principle herein, once an appropriate assetmodel is established for a given energy asset or group of energy assets,different candidate operating schedules may be applied to the model tosimulate how the energy profile EP(t) of the asset(s) is affected as afunction of time, over a given time period T, by the different operatingschedules.

An example technique for facilitating determination of optimal operatingschedule for energy cost reduction and/or revenue generation fromwholesale electricity markets according to various examples disclosedherein is as follows. In this example, the system includes an energyconsuming asset, a controller of the energy storage asset, and acontrollable energy consuming asset. A plurality of first candidateoperating schedules is selected for the controller, and a plurality ofsecond candidate operating schedules is selected for the energyconsuming asset. Each second candidate operating schedule for the energyconsuming asset is different from the BAU operating schedule for theenergy consuming asset. The plurality of first and second candidateoperating schedules are successively applied to the mathematical modelto generate corresponding plurality of simulated energy profiles for theenergy storage asset and the energy consuming asset. A plurality ofprojected net energy-related costs to the energy customer are computed,where each projected net energy-related cost is computed based at leastin part on the representative CBL energy profile and the simulatedenergy profiles corresponding to the respective first and secondcandidate operating schedules and the forecast wholesale electricityprice. Respective ones of the first and second candidate operatingschedules corresponding to one simulated energy profile of the pluralityof simulated energy profiles that results in a minimum netenergy-related cost of the plurality of net energy-related costscalculated are selected as an optimal first operating schedule and anoptimal second operating schedule. That is, namely, this technique canbe implemented to simulate how energy assets consume/generateelectricity based on different candidate operating schedules for theasset(s), and to select a particular operating schedule that facilitatesa particular economic goal of the energy customer.

In another example, the operating schedules for the energy storage assetand energy consuming asset can be calculated in tandem based onminimizing the net energy-related costs (NEC), as discussed in greaterdetail below.

Operating Schedules and Constraints

In considering various operating schedules SP(t) that may be applied tothe asset model so as to simulate a corresponding energy profile EP(t),in some instances SP(t) may not be varied freely. Such limitations oncandidate operating schedules may be due at least in part to physicallimitations of the asset(s) being modeled, and/or limitations onoperation of the asset(s) dictated by the energy customer itself. Forexample, in some instances the customer may want to constrain the rangein which the magnitude of SP(t) may be varied at any given time, and/orthe customer may wish to designate particular periods of time (e.g.,within the given time period T of interest) during which particularvalues of SP(t) cannot be changed (or only changed in a limited manner).

For purposes of illustration, again consider a transport vehicle with anHVAC system. The customer may specify that, in considering candidateoperating schedules SP(t) for the transport vehicle, temperature setpoints (i.e., the magnitude of SP(t) in this example) must remain in arange of from between 65 to 75 degrees F. in any proposed operatingschedule; furthermore, the customer may dictate that during a certaintime frame, the temperature set point may not exceed 70 degrees F. Ingeneral, magnitude and/or timing limitations placed on a candidateoperating schedule SP(t) for one or more modeled assets are referred toherein as “constraints” on the operating schedule.

The concept of candidate operating schedules for one or more modeledenergy assets subject to one or more “constraints” is denoted herein as:

-   -   SP(t)|_(Constraints)≡operating schedule for one or more energy        assets subject to constraints

In an example, the system includes an energy storage asset, andconstraint may be placed on the allowed state of charge (SOC) of theenergy storage asset. For example, the constraint may be placed that theSOC does should not be allowed to fall below a minimal SOC value (i.e.,not too depleted) and/or that the SOC does should not be allowed to goabove a maximal SOC (i.e., not overly-charged).

Business-As-Usual (BAU) Conditions and Customer Baseline (CBL) EnergyProfiles

Once an appropriate asset model is established for a given energy assetor group of energy assets, a particular operating schedule of interestin some examples is referred to herein as a “typical” or“business-as-usual” (BAU) operating schedule (also referred to herein as“BAU conditions”), denoted as SP(t)_(BAU). In particular, “BAUconditions” refer to an operating schedule that an energy customer wouldtypically adopt for its energy asset(s), absent the incentive to reduceenergy costs and/or earn energy-related revenue from wholesaleelectricity markets. Again turning to the example of a transport vehiclefor purposes of illustration, absent any incentive to change itsbehavior, during a summer season in which cooling is desired an energycustomer may typically set the thermostat (i.e., temperature set points)for the transport vehicle at 72 degrees F. from 9 PM to 9 AM, and at 68degrees F. from 9 AM to 9 PM; this can be represented conceptually usingthe notation adopted herein as:

${{SP}(t)}_{BAU} = {\begin{Bmatrix}{72,} & {{9{PM}} < t < {9{AM}}} \\{68,} & {{9{AM}} < t < {9{PM}}}\end{Bmatrix}.}$

When a typical operating schedule SP(t)_(BAU) is applied to the assetmodel, the particular energy profile generated by the model is a specialcase referred to herein as a simulated “customer baseline” (CBL) energyprofile, denoted as CBL(t). Using the example relationship given in Eq.2 above (which includes consideration of weather information), thespecial case of a CBL energy profile may be conceptually representedmathematically as:

F(SP(t)_(BAU) ,W(t))=CBL(t),   Eq. 6

where the typical operating schedule SP (t)_(BAU) is an argument of thefunction F (in this example together with the weather information W(t)),and the CBL energy profile of the modeled asset(s) as a function of timeis denoted as CBL(t).

Although consideration of weather information W(t) is included in theexample above, it should be appreciated that the simulation of acustomer baseline (CBL) energy profile in other examples may notconsider weather information (as such information may not be relevant tothe energy profile of the asset(s) in question). It should also beappreciated that while the simulation of a CBL energy profile may beuseful for mathematical optimization techniques employed in someexamples to facilitate energy cost reduction and/or revenue generationfrom particular wholesale electricity markets (e.g., economic demandresponse “energy markets”), simulation of a CBL energy profile may notbe applicable or necessary in other examples to facilitate energy costreduction and/or revenue generation from wholesale electricity markets.

Objective Cost Functions and Optimal Control

For purposes of the present disclosure, an “objective cost function”specifies all energy-related costs and energy-related revenuesassociated with operating one or more modeled energy assets of an energycustomer so as to achieve a particular economic goal (an economic“objective”). In one aspect, an objective cost function incorporates thefunction(s) F representing the mathematical model for one or more energyassets, and specifies an energy customer's “net energy-related cost”(e.g., in dollars) associated with operating the modeled asset(s) over agiven time period T. The energy customer's net energy-related cost asgiven by the objective cost function is denoted herein as NEC$:

-   -   NEC$≡net energy-related cost to operate one or more energy        assets.        As discussed in greater detail below, objective cost functions        providing a net energy-related cost NEC$ according to different        examples may have a variety of respective cost and revenue        terms, based at least in part on the types of asset(s) being        operated and the particular revenue-generation objective(s)        (e.g., the particular wholesale electricity market(s) from which        revenue is being sought).

For example, in some examples, the energy-related costs included in theobjective cost function may include “actual” energy-related costs (e.g.,retail electricity costs, wholesale electricity costs representingrevenue earned by the energy customer, etc.). In some examples, theenergy-related costs included in the objective cost functionadditionally or alternatively may include “indirect” energy-relatedcosts, such as convenience/comfort costs associated with the energycustomer's adoption of a suggested operating schedule different than theBAU operating schedule (the convenience/comfort cost represents an“indirect” cost associated with a change in the customer's behavior withrespect to operating its asset(s), based on the incentive of possibleenergy-related revenue from the wholesale electricity markets). In anexample, energy-related costs included in the objective cost functionmay include reliability costs associated with voltageNAR control in amicrogrid application. Similarly, an objective cost function may includeone or more terms specifying energy-related revenues corresponding toone or more wholesale electricity markets (e.g., “energy markets,”“synchronized reserve,” “regulation”).

To provide a preliminary illustration of concepts germane to anobjective cost function specifying a net energy-related cost NEC$, anexample relating to economic demand response revenue from the wholesaleelectricity “energy markets” is first considered. To this end, retailelectricity prices (i.e., what the energy customer pays a “utility” forelectricity usage) and wholesale electricity-related product pricesavailable to the energy customer respectively are denoted as:

-   -   Retail$(t)=price of electricity from a retail electricity        provider (“utility”); and        Wholesale$(t)=price of electricity-related product on applicable        wholesale electricity market, where the retail electricity price        Retail$(t) and the wholesale electricity-related product price        Wholesale$(t) may vary independently of each other as a function        of time. In an example, the units of the retail electricity        price Retail$(t) and the wholesale electricity-related product        price Wholesale$(t) are $/MWh.

The wholesale price Wholesale$(t) can be dictated by (e.g., based atleast in part on) the “locational marginal price” (LMP) as a function oftime, as noted above (see Background section). However, depending on agiven wholesale electricity market and/or a particularelectricity-related product in question, it should be appreciated thatthe wholesale price Wholesale$(t) may be based on other and/oradditional factors. Also, practically speaking, wholesale prices are notcontinuous functions of time; rather, as discussed above, wholesaleprices based on the LMP may be calculated periodically at specifiednodes of the grid (e.g., every 5 minutes, every half-hour, every hour)depending on the particular market in which the energy customer isparticipating. Accordingly, it should be appreciated that Wholesale$(t)typically is a discrete function of time, with t having some periodicity(e.g., 5 minutes, 30 minutes, 60 minutes).

Given the notation above for retail and wholesale prices, the energycustomer's modeled retail electricity costs (or “supply costs”), foroperating one or more modeled electricity-consuming assets pursuant to aparticular operating schedule SP(t) applied to an asset model, isdenoted herein as Supply$(t), given by:

Supply$(t)=EP(t)*Retail$(t),   Eq. 7

wherein EP(t) is the energy profile of the modeled asset(s) (e.g., givenby any of Eqs. 1-4 above).

For the energy storage asset, the energy customer's “supply costs” forcharging the asset can be denoted herein as Supply$(t)_(ES), given by:

Supply$(t)_(ES) =EP(t)*Retail$(t),   Eq. 8

wherein EP(t) is the energy profile of the modeled energy storageasset(s). Since the energy profile for an energy storage asset can berepresented based on a charge rate (C_(t)) for a time step (t<T) overthe amount of time of charging (Δt), the supply costs can be expressedas:

Supply$(t)_(ES) =C _(t) *Δt*Retail$(t).   Eq. 9

The charge rate (C_(t)) may be the maximum charge rate of the energystorage asset, or a charge rate less than the maximum charge rate. Forexample, in different examples herein, the output of the controller maymodify the charge rate of the energy storage asset to values that areless than the maximum charge rate.

If the system includes an energy storage asset and an energy generatingasset, the total supply costs can be expressed, in a non-limitingexample, as the energy storage asset (Supply$(t)_(ES)) reduced by a costamount based on the amount of energy provided by the energy generatingasset (EG_(k)). In an example, the total supply costs can be expressedas:

Supply$(t)_(total)=(C _(k) −EG _(k))*Δt*Retail$(t).   Eq. 10

Supply costs may also apply to the system by virtue of the reduction inlife of the energy storage asset. An energy storage asset may have alimited life depending on its rating of expected charge/dischargecycles. A portion of the costs associated with ultimately replacing anenergy storage asset at the end of its lifetime may be included in thesupply costs based on the number of charge/discharge cycles it isexpected to undergo when implemented in an energy market and/or aregulation market as described herein. The lifetime reduction supplycosts may also depend on the number of kWh is used in each charge ordischarge cycle, and/or for what length of time the energy storage assetis used in a market (energy, regulation, etc.). For example, thecontribution to the supply costs based on the replacement cost(Replacement$) may be computed according to the expression:

Supply$(t)_(LIFE)=Replacement$/n   Eq. 11

where n represents an effective number of charge/discharge cycles. Theeffective number of charge/discharge cycles can depend on the number ofcycles the asset is expected to undergo when implemented in an energymarket and/or a regulation market, the number of kWh is used in eachcharge or discharge cycle, and/or for what length of time the energystorage asset is used in a given market. This lifetime supply cost wouldbe additive to any of the expressions for supply costs described hereinfor a system that includes an energy storage asset.

With respect to economic demand response revenue from the wholesaleelectricity energy markets, in the present example it is presumed thatthe energy customer is amenable to operating its energy asset(s)pursuant to a candidate operating schedule that is different than its“typical operating schedule” or BAU conditions (i.e., SP(t)_(BAU)), suchthat the energy profile EP(t) of the asset(s) will be on average lowerthan the customer baseline CBL(t) (see Eq. 6 and related descriptionabove). Altering the energy profile of the asset(s) with respect to thecustomer baseline, pursuant to a change in behavior represented by acandidate operating schedule different than BAU conditions, provides thesource of opportunity for generating economic demand response revenuefrom the wholesale electricity energy markets. Accordingly, a wholesaleelectricity energy market “demand response revenue,” denoted herein asDR$(t)_(EM), is given generally by:

DR$(t)_(EM)=max {0,[(CBL(t)−EP(t))*Wholesale$(t)]}.   Eq. 12

In an example, the DR$(t) represents the net difference between actualnet metered load and the BAU load. The participation of any component ofthe energy asset in the energy market, regulation market or spinningreserve market is included in the computation of DR$(t) to the extentthey affect the value of the metered load. In addition, the energygenerated by any energy generating asset that is part of the energyasset may also be included in the computation of DR$(t) to the extentthis behind-the-meter generated energy affects the value of the meteredload.

For an energy storage asset in an energy market, a demand responserevenue may be denoted herein as DR$(t)_(ES), is given generally by:

DR$(t)_(ES)=(0−(−(D _(t)))*Δt*Wholesale$(t).   Eq. 13

As described herein, a system that includes an energy storage asset canparticipate in both an energy market (at a price of Wholesale$(t)) andin a regulation market (at a price of regulation$(t)). In this example,the demand response revenue may be computed herein as DR$(t)_(ES),denoted by:

DR$(t)_(ES)=(εD _(t))*Δt*Wholesale$(t)+(γD _(t))*Δt*regulation$(t)   Eq.14

where D_(t) denotes the discharge rate of the energy storage asset at atime step. Where the system participates in the energy market and theregulation market at different points in time during overall time periodT, both multipliers of the discharge rate, ε and γ, may be equal to 1.In different examples herein, the output of the controller may modifythe discharge rate of the energy storage asset to values that are lessthan the maximum discharge rate. Using the principles of virtualpartitioning described herein, by apportioning an output of thecontroller in communication with the energy storage asset, a portion ofthe discharge rate may be directed to the regulation market and anotherportion directed to the energy market during a given time step. As anon-limiting example, the operating schedule determined as describedherein may cause the controller to discharge the energy storage asset ata discharge rate of εD_(t) to the energy market, while concurrentlyrespond to the regulation market at a discharge rate of γD_(t) alongshorter timescales (such as but not limited to at 2-second intervals orminute-by-minute time intervals). Here, the constraint on the values maybe ε+γ≦1 if D_(t) represents the maximum discharge rate of the energystorage asset.

In a non-limiting example, where the regulation price is not based onthe discharge rate, but rather depends only on the time period ofcommitment of the energy storage asset to the regulation market, thedemand response revenue may be computed as:

DR$(t)_(ES)=(εD _(t))*Δt*Wholesale$(t)+regulation$(t)*Δt   Eq. 15

In another example, the demand response revenue for a system thatincludes an energy storage asset and an energy generating assetparticipating in an energy market may be computed as:

DR$(t)_(ES+EG)=(D _(t))*Δt*Wholesale$(t)+(E _(EG))*Wholesale$(t)   Eq.16

where D_(t) denotes the discharge rate of the energy storage asset at atime step and E_(EG) denotes the energy provided by the energygenerating asset.

According to the principles described herein, a demand response may alsobe generated for a system that includes an energy storage asset and anenergy generating asset participating in both an energy market and aregulation market.

Based on the equations for supply costs and demand response above, anexample of an objective cost function to provide a net energy-relatedcost NEC$ over a given time period T for operating the modeled asset(s),considering both retail electricity supply costs and demand responserevenue can be computed based on the expression:

$\begin{matrix}{{{NEC}\; \$} = {\sum\limits_{t}^{T}\; {\left( {{{Supply}\; {\$ (t)}} - {{DR}\; {\$ (t)}}} \right).}}} & {{Eq}.\mspace{11mu} 17}\end{matrix}$

In one example, an objective cost function as exemplified by Eq. 17 maybe provided to an optimizer (a particularly-programmed processor, alsoreferred to as a “solver”; such as processor unit 13 of FIG. 2) thatimplements a mathematical optimization process to determine a suggestedoperating schedule for the energy asset(s) over the time period T thatminimizes the net energy-related cost NEC$. Accordingly, the optimizersolves for:

$\begin{matrix}{{Min}\left\lbrack {\sum\limits_{t}^{T}\; \left( {{{Supply}\; {\$ (t)}} - {{DR}\; {\$ (t)}}} \right)} \right\rbrack} & {{Eq}.\mspace{11mu} 18}\end{matrix}$

By substituting the pertinent equations for supply costs and demandresponse (which depends on the energy assets in a given system) backinto Eq. 18, the various informational inputs provided to the optimizermay be readily ascertained.

As a non-limiting example, for a system that is participating in theenergy market, the various informational inputs provided to theoptimizer may be readily ascertained as follows:

$\begin{matrix}{{{Min}\left\lbrack {\sum\limits_{t}^{T}\; \left\{ {\left( {{{EP}(t)}*{Retail}\; {\$ (t)}} \right) - \left( {\max \mspace{14mu} \left\{ {0,\left\lbrack {\left( {{{CBL}(t)} - {{EP}(t)}} \right)*{Wholesale}\; {\$ (t)}} \right\rbrack} \right\}} \right)} \right\}} \right\rbrack},} & {{Eq}.\mspace{11mu} 19}\end{matrix}$

where from Eq. 2

EP(t)=F(SP(t)|_(Constraints) ,W(t)),

and from Eq. 6

CBL(t)=F(SP(t)_(BAU) ,W(t)),

where again it is presumed for purposes of illustration that weatherinformation W(t) is relevant in the present example. From the foregoing,it may be seen that one or more of the following inputs may be providedto the optimizer in various examples:

-   -   F—one or more functions defining the mathematical model for the        energy asset(s);    -   SP(t)_(BAU)—BAU or “typical” operating schedule for the energy        asset(s);    -   Constraints—any timing and/or magnitude constraints placed on        candidate operating schedules for the energy asset(s);    -   W(t)—weather information as a function of time (if appropriate        given the type of energy asset(s) being operated);    -   u(t)—control vector for the controller in communication with the        energy storage asset;    -   Retail$(t)—retail price of electricity as a function of time;    -   Wholesale$(t)—wholesale price of electricity-related product as        a function of time;    -   Regulation$(t)—regulation price in regulation market as a        function of time; and    -   NEC$—the objective cost function describing the energy        customer's net energy-related cost associated with operating the        modeled energy asset(s).        Based on the foregoing inputs, the optimizer solves Eq. 19 by        finding an “optimal” operating schedule for the energy asset(s),        denoted herein as SP(t)_(opt), that minimizes the net        energy-related cost NEC$ to the energy customer:    -   SP(t)_(opt)≡“optimal” or suggested operating schedule for one or        more energy assets

In various implementations described herein, the optimizer may receiveone or more inputs, including but not limited to, the weatherinformation W(t), the retail electricity price Retail$(t), and thewholesale price of the electricity-related product Wholesale$(t) (andthe regulation price (regulation$(t))) as forecasted values providedfrom a third-party source, for the time period T over which theoptimization is being performed.

While a given optimizer in a particular implementation may employvarious proprietary techniques to solve for the minimization of anobjective cost function according to various examples of the principlesherein, conceptually the optimization process may be generallyunderstood as follows. In various implementations discussed herein, theoptimizer generates the operating schedule using the model of the systemthrough an optimal control procedure. In the various exampleimplementations, the optimizer determines an optimal operating scheduleover the defined time period (I) by optimizing an objective costfunction. For example, the optimizer can be implemented to determine theoperating schedule that generates the energy-related revenue byminimizing a function representing the net energy-related costs of thesystem over the time period (I). The net energy-related costs can becomputed based on the supply costs and the demand response revenue asdescribed herein, including in Eqts. 1-19 above. The optimizer optimizesthe objective cost function over the entire defined time period (T) togenerate the operating schedule. The generated operating schedule caninclude suggestions, for different specific time intervals within theoverall time period T, for when the controller can be used to implementthe energy storage asset in the energy market, in the regulation market,or in both the energy market and regulation market (through dynamicpartitioning).

In a non-limiting example of an implementation of the optimizer, somenumber N of candidate operating schedules SP(t)|_(Constraints) for themodeled asset(s) (together with weather information W(t), if appropriatebased on a given objective function) can be successively applied to theasset model given by the function(s) F to generate simulated energyprofiles EP(t) corresponding to the candidate operating schedules (seeEqs. 1-4). A net energy-related cost NEC$ given by the objective costfunction is calculated for each such simulated energy profile EP(t) (seeEq. 17), and the candidate operating schedule that minimizes theobjective cost function (i.e., the “optimal” operating scheduleSP(t)_(opt) that minimizes the net energy-related cost NEC$) is selectedas the suggested operating schedule to be provided to the energycustomer.

As noted earlier, the example above in connection with the objectivecost function of Eq. 17 is based on actual energy-related costs (e.g.,retail electricity cost) Supply$(t). In other examples, theenergy-related costs included in a given objective cost functionadditionally or alternatively may include “indirect” energy-relatedcosts, such as “convenience/comfort” costs associated with the energycustomer's adoption of a suggested operating schedule SP(t)_(opt)different than its typical operating schedule SP(t)_(BAU). In one aspectof such examples, a convenience/comfort cost represents an “indirect”cost in that it does not necessarily relate to actual energy-relatedexpenditures, but rather attributes some cost (e.g., in dollars)relating to a change in the customer's behavior with respect tooperating its asset(s), based on the incentive of possibleenergy-related revenue from the wholesale electricity markets.

Accordingly, in some examples, an alternative objective cost functionsimilar to that shown in Eq. 17 may be given as:

$\begin{matrix}{{{{NEC}\; \$} = {\sum\limits_{t}^{T}\; \left( {{{Comfort}\; {\$ (t)}} + {{Supply}\; {\$ (t)}} - {{DR}\; {\$ (t)}}} \right)}},} & {{Eq}.\mspace{11mu} 20}\end{matrix}$

where Comfort$(t) represents a convenience/comfort cost associated witha change in the energy customer's behavior with respect to operating itsasset(s). In an example where the energy-related costs included in theobjective cost function include reliability costs, they would beincluded in the computation (such as in Eq. 12) similarly to theComfort$(t).

A convenience/comfort cost Comfort$(t) may be defined in any of avariety of manners according to different examples. For example, in oneimplementation, a convenience/comfort cost may be based at least in parton a difference (e.g., a “mathematical distance”) between a givencandidate operating schedule and the typical operating schedule (BAUconditions) for the modeled asset(s)—e.g., the greater the differencebetween the candidate operating schedule and the typical operatingschedule, the higher the convenience/comfort cost (there may be moreinconvenience/discomfort attributed to adopting a “larger” change inbehavior). This may be conceptually represented by:

Comfort$(t)=G[|SP(t)|C _(Constraints) −SP(t)_(BAU)|],   Eq. 21

where G specifies some function of the absolute value of the“difference” between a candidate operating schedule (e.g., in a giveniteration of the optimization implemented by the optimizer) and thetypical operating schedule.

To provide an example of how Eqs. 20 and 21 may be employed in anoptimization process to determine a suggested operating scheduleSP(t)_(opt) for an energy customer according to one example, againconsider a transport vehicle operated by the energy customer, for whicha given operating schedule SP(t) is constituted by a temperature setpoint as a function of time. If T(t)_(BAU) represents the temperatureset points constituting a typical operating schedule, andT(t)|_(Constraints) represents different temperature set pointsconstituting a candidate operating schedule that may be adopted tofacilitate energy-cost reduction and/or revenue generation, theconvenience/comfort cost Comfort$(t) in this example may be defined as a“temperature set point deviation” T_(dev)(t), according to:

Comfort $(t)≡T _(dev)(t)=A(|T(t)|_(Constraints) −T(t)_(BAU)|),   Eq. 22

where A is a constant that converts temperature units to cost units(e.g., degrees F. to dollars). In an example, A may be adjustable foreach individual time step, so A may be represented as a vector. Eq. 22specifies that there is a greater “indirect” cost associated withcandidate operating schedules having temperature set points that deviatemore significantly from the typical temperature set points (albeitwithin the constraints provided by the energy customer). In this manner,as part of the optimization process, potential revenue from thewholesale electricity markets may be “tempered” to some extent by aperceived cost, included in the objective cost function (see Eq. 20),that is associated with the inconvenience/discomfort of deviatingsignificantly from the typical operating schedule.

In the example above, although the multiplier A in Eq. 22 is discussedas a conversion constant, it should be appreciated that in otherexamples A may be an arbitrary function having as an argument theabsolute value of the difference between a candidate operating scheduleand the typical operating schedule as a function of time. Moregenerally, it should be appreciated that a convenience/comfort costComfort$(t) is not limited to the “temperature-related” example providedabove in connection with a transport vehicle, and that otherformulations of a convenience/comfort cost as part of an objectivefunction are possible according to various examples of the principlesherein.

In yet other examples of objective cost functions, different cost andrevenue terms of a given objective cost function may includecorresponding “weighting factors” (e.g., specified by the energycustomer), so as to ascribe a relative importance to the energy customerof the respective terms of the objective cost function in arriving at asuggested operating schedule SP(t)_(opt). For example, in someinstances, an energy customer may want to emphasize the importance ofincreasing prospective demand response revenue DR$(t) vis a visdecreasing supply costs Supply$(t) in solving the optimization problemto arrive at a suggested operating schedule; similarly, in otherinstances, an energy customer may want to emphasize convenience/comfortcosts Comfort$(t) vis a vis increasing prospective demand responserevenue DR$(t) in solving the optimization problem to arrive at asuggested operating schedule. The ability of an energy customer totailor a given objective cost function according to weighting factorsfor respective terms of the objective cost function provides an“elasticity” to the optimization process. Using the objective costfunction given in Eq. 20 above as an example, in one example suchweighting factors may be included in the specification of an objectivecost function as respective term multipliers:

$\begin{matrix}{{{{NEC}\; \$} = {\sum\limits_{t}^{T}\; \left\lbrack {\left( {\alpha^{*}{Comfort}\; {\$ (t)}} \right) + \left( {\beta^{*}{Supply}\; {\$ (t)}} \right) - \left( {\gamma^{*}{DR}\; {\$ (t)}} \right)} \right\rbrack}},} & {{Eq}.\mspace{11mu} 23}\end{matrix}$

where α, β, and γ constitute the weighting factors. In an example,α+β+γ=1. In another example, α+β+γ≠1.

In another example, the value of a weighting factor may differ atvarious points during the day. For example, if it is preferred that theComfort$(t) takes a bigger part in the objective cost functioncomputation at certain points during the day, the factor α may beincreased relative to the other weighting factors.

In an example implementation, the operating schedule can be generatedthrough applying an optimization using a net-energy related costfunction based only on the energy market. The result of the optimizationcan be used to provide recommendation for time intervals for the energycustomer to participate in the energy market, regulation market, or boththe energy market and the regulation market. For example, based on theresults of the optimization, the operating schedule may determine thatany excess charge/discharge capacity of the controller of the energystorage system may be committed to the regulation market on anhour-by-hour basis. For example, it can be determined that the anyexcess charge/discharge capacity of the controller may be committed tothe regulation market during the first 15 time intervals. Theoptimization may make such a determination depending on whether theforecast regulation price in the regulation market in this time intervaloffers opportunity for energy-related revenue during this time intervalor if considered in the context of the global optimization over timeperiod T. In an example, such a determination may be made depending onwhether the SOC of the energy storage asset is feasible for its use inthe regulation market. For example, it may be preferable for the energystorage asset to be near around a 50% SOC for it to be applicable to theregulation market. In addition, if it is decided to commit the energystorage asset to the regulation market for a time interval, e.g., forone or more 1-hour time intervals, the optimization described herein maybe re-performed based on the new input state of the system. Such newinputs can include the state of charge of the energy storage asset afterits commitment to the regulation market ends. In another non-limitingexample, the optimization may evaluate different SOC initial inputs toassess whether “recovery” from the regulation market is feasible forlater participation in the energy market.

In an example, a predetermined threshold value of wholesale electricityprice can be set at which it is decided that the excess charge/dischargecapacity of the controller will be committed to the regulation market.Based on the results of the optimization, a predetermined thresholdvalue of the LMP price, indicated by the dashed horizontal line, may beset. In addition, it may be determined that the first time interval ofcharging the energy storage asset occurs during the time period that Tcoincides with the time interval during which the forecast wholesaleelectricity price falls below the predetermined threshold value. It mayalso be determined in the operating schedule that a second time intervalof discharging the energy storage asset occurs coincides with a timeinterval during which the forecast wholesale electricity price exceedthe predetermined threshold value.

While the discussion above of example objective cost functions andoptimization of same to generate suggested operating schedules forenergy assets has been based at least in part on economic demandresponse revenue from wholesale electricity energy markets (and in someparticular examples involving transport vehicles), it should beappreciated that the disclosure is not limited in this respect; namely,according to other examples, objective cost functions may be formulatedand optimized to achieve a wide variety of energy-related objectivesassociated with different types of energy assets and revenue generationopportunities from wholesale electricity markets. For example,computation based on revenue from the regulation market has also beendescribed herein above, and optimization based on the wholesale priceand the regulation price are described herein below. In other examples,the principles herein can be applied to other markets, such as thespinning reserve market.

Generating an Operating Schedule for Deriving Energy-Related Revenue

As discussed above, the output of an optimization process to minimize anenergy customer's net energy-related cost NEC$ (e.g., as specified by anobjective cost function) is typically provided as a suggested operatingschedule SP(t)_(opt) for one or more energy assets. Generally speaking,the suggested operating schedule SP(t)_(opt) may comprise one or moreset point values as a function of time that take into consideration allof the energy customer's modeled and controllable energy assets.

For example, in some instances involving multiple individually modeledand controllable energy assets, the suggested operating scheduleSP(t)_(opt) may comprise multiple time-varying control signalsrespectively provided to corresponding controllers for the differentenergy assets. In other cases, the energy customer may have an energymanagement system (EMS) that oversees control of multiple energy assets,and the suggested operating schedule SP(t)_(opt) may comprise a singlecontrol signal provided to the energy customer's EMS, which EMS in turnprocesses/interprets the single control signal representing thesuggested operating schedule SP(t)_(opt) to control respective energyassets.

In examples in which the energy customer normally operates its energyasset(s) according to a typical operating schedule SP(t)_(BAU) (absentany economic incentive to change its energy-related behavior), thesuggested operating schedule SP(t)_(opt) may be conveyed to the energycustomer in the form of one or more “bias signals,” denoted herein byBias(t). In particular, one or more bias signals Bias(t) may represent adifference between the suggested operating schedule and the typicaloperating schedule as a function of time, according to:

Bias(t)=SP(t)_(opt) −SP(t)_(BAU).   Eq. 24

Eq. 24 applies in certain cases. In a more general case, the Biasoffsets the “demand level” over components of the energy asset. The biassignal (sent to a EMS) may cause controllers to take actions such as butnot limited to load sheddings (including shutting off non-essentialloads) and modifying the amount of energy used to push a train (e.g.,out of a station) or to start a car of a carriage system.

In an example, in response to the bias signal is sent to the EMS, theEMS may make changes to operation settings of components of the energyasset.

Following is a description of the different markets, including energymarkets and regulation markets, to illustrate how each market can affectthe operation of an energy asset.

Dynamic Virtualization

Dynamic virtualization is an integrated solution for energy generationand storage involving energy assets, such as batteries and solargenerators. This uses a version of examples with virtual partitioning ofan energy storage asset. Dynamic virtualization can be used toco-optimize energy storage assets and solar generation across differentenergy markets or other uses. These markets or uses may include (1)electric energy provided over the grid to the energy market, and (2) theancillary services market (which may include regulation, which isfocused on regulation of power frequency and voltage on the grid) or (3)use of the storage device to maintain power quality at the owners'facilities.

Dynamic virtualization uses examples of systems with the virtualpartitioning of the battery or other type of energy storage asset intovirtual separate batteries, each virtual energy storage asset beingallocated to separate markets or functions, such as participating in theenergy market, and the ancillary services (regulation) market or use tomaintain power quality at the premise. The virtual partition of thebatteries is not physical, but is instead an allocation of energystorage asset capacity to various markets or uses. This virtualpartition by allocation is dynamic in that it can be constantly changedin response to changing price points and performance requirements duringthe day.

There are rapid swings in load on the spot electric energy market. Inorder to maintain electrical balance on the grid and regulate consistentpower and voltage on the grid over short periods of time, for example,over periods of four seconds, fifteen seconds, or one minute, the gridoperator sends out signals to change generation to match the loadchanges. Batteries are particularly well suited to respond to theseshort response time signals.

With examples of the principles herein, energy storage assets such asbatteries can be applied to swing between the markets for energy andancillary services for regulation of the grid or for the maintenance ofpower quality at the energy storage asset owner's facility. In the past,batteries were not purchased and installed for the purpose of providingregulation services, because batteries tend to be too expensive for thispurpose alone. Most regulation services now come from gas poweredgenerators providing about 1-10 megawatts, and these energy assets taketime to turn on and off. Industrial batteries, however, are instant onand off and usually provide power in the 1 megawatt range—and canrespond to grid operator signals in milliseconds.

In the past, energy storage and energy storage asset facilities wereusually purchased with the intent to provide backup power for theowners, in case the electric power grid goes down or temporarilyprovides inadequate power. However, once the battery or other type ofenergy storage assets are installed to satisfy backup capacity for theowner, they may also to some extent be active in the regulation marketto regulate the power and voltage on the grid, and in the energy market,to sell power into the grid in response to real-time pricing changes (orto cut the user's demand on the grid). For example, energy storageassets may discharge to the grid during high LMP price hours.

Energy storage assets may include batteries, ice units, compressed air,or other technologies to store energy on site by users and generators ofpower. Batteries may be of any type, including lithium ion, lead acid,flow batteries, dry cell batteries, or otherwise.

Solar generators of power may include solar panels, solar cells, anyother photovoltaic power generator, or any means for generating powerfrom sunlight. This may also include generation of electricity fromsteam or similar use of liquid to gas phase generation from sunlight, togenerate electricity.

The energy market involves generating power, distributing power into thegrid, and drawing power out of the grid, each at a price. This ismeasured in terms of megawatt hours that are the amount of powerdelivered. Energy is delivered for sustained periods of time, such asfor 15 minutes or more.

The capacity market is measured in terms of megawatts of capacity. Inthis market, a seller makes their facilities available to generateelectricity when needed and holds them in reserve for that purpose, butmay never actually distribute energy into the grid rather than just beon-call. This, in effect, pays the seller to be available and impactsthe reliability of the grid.

The ancillary market includes regulation of frequency and voltage in thegrid, and the provision of an operating reserve. The regulation of thevoltage in the grid involves discharging energy into the grid orabsorbing energy from the grid in small increments, frequently, forshort periods of time, and very rapidly.

Smart grid services increasingly rely on new technologies such asrenewable energy and large-scale storage resources. Unfortunately, thelife-cycle costs associated with such resources, when takenindividually, are still high compared with more traditional forms ofenergy production. In addition, the desired proliferation of distributedand renewable resources on the power grid introduces new threats to itsreliable operation, as they are subject to unpredictable drops inoutput, such as when the wind stops blowing. Consequently, both economicand reliability issues introduce substantial obstacles to a highpenetration of those technologies in the power grid.

By themselves, storage resources such as electrical batteries arepresently high cost options. Likewise, photovoltaic generation and windturbines are comparatively quite expensive and their intermittencycreates new strains on the power grid.

However, when optimally managed by various examples disclosed herein toprovide timely support to the power grid, the net cost of electricalstorage can be substantially reduced, as the result of payments by thegrid operator (ISO/RTO) provides for facilities that can be called on toprovide such support. Also, combining energy storage with intermittentgeneration makes technologies such as wind and solar more predictable onthe grid, and hence, more valuable.

Examples, including dynamic virtualization, can dramatically improve theeconomics of renewable generation and storage technologies, byco-optimizing their operation to participate in the various energy andancillary services (including regulation) markets and thus maximizetheir economic benefits.

Examples focus on the economics of batteries and energy storage and, byproviding energy resource optimization and a gateway to the wholesalemarkets, can help facility managers deploy a comprehensive energystorage solution that can cost-effectively meet an organization'sbusiness objectives.

More broadly, when optimally coupling energy storage with renewablegeneration, various examples redefine the economics of such resources,while providing firm, dispatchable virtual generation that supports thereliability objectives of the power grid. Thus, by integratingdistributed resources into virtual generation via system operatordispatch, examples can help enable the acceleration of renewable energygeneration technologies such as solar and wind.

Systems Including Energy Storage Assets

Large-scale storage is widely seen as a necessary piece of the smartgrid and a key component of America's electricity future. Thisrecognition is driven by the following factors: (1) the growing adoptionof intermittent renewable power sources; (2) state and nationwide budgetshortfalls, leading local governments to seek cost-effective solutionsfor maintaining America's aging infrastructure; and (3) the widespreadbelief that electric vehicles (“EVs”) will materially grow their marketshare over the next 5 to 15 years.

In this context, stakeholders have been looking for ways to acceleratethe development and implementation of grid-level storage. Effectivebattery and other energy storage asset solutions can take unpredictableenergy resources and turn them into reliable power, while matchingelectricity supply to demand; they play a crucial role in fosteringmicrogrids and distributed generation, viable alternatives to expandingthe U.S.'s power infrastructure; and they can address the new and uniqueconcerns created by EVs, such as helping to maintain grid stability andgiving utilities and grids more control over energy dispatch.

A key concern with batteries has long been their high upfront cost andlong payback periods. Various examples address this by providingbattery-owners a robust gateway to the wholesale electricity markets,thus unlocking new streams of revenues that increase their return oninvestment. This may also apply to other types of energy storage assets.

Various examples provide processor-executable instructions (includingsoftware solutions) that optimizes participation in wholesale markets byproviding energy storage asset owners with dynamic virtualization, aservice that continuously re-partitions the energy storage asset fordifferent markets and uses, chiefly real-time energy, and regulation,and power quality control, in an optimized manner, based on pricing andweather data, retail electricity rates, and characteristics of theenergy storage asset and its host site.

For large retailers and supermarkets, backup generation is a necessarybut often expensive proposition. The nation's largest big box chainshave taken a variety of approaches to minimizing the costs of providingsubstitute power in the case of an emergency or brownout; but for manystores, their only choice to date has been inefficient and costly dieselgenerators.

Examples with dynamic virtualization optimally manage an energy storageasset's state of charge based on the revenue producing opportunities inthe wholesale market, as well as the organization's business objectives,such as providing backup power to critical loads for a given period oftime. Thus, when paired with these examples, the energy storage assetbecomes an energy resource that will concurrently: (1) participate inthe energy markets by providing a way to shift the net load of afacility from high- to low-price periods; (2) participate in thefrequency regulation market by responding to real-time signals from thegrid operator; (3) participate in other wholesale markets, such asenergy and synchronized reserve; and (4) provide reactive/voltagesupport to the microgrid/distribution grid.

Examples enable the energy storage asset to maximize revenues from thevarious wholesale markets, while maintaining its ability to achieve itsmain objective of providing a reliability service to the organization.To achieve this, examples herein describe virtualization of the energystorage asset and creating dynamic “energy storage asset partitions,” ina manner similar to the way computing resources are virtualized. Throughits optimization capability, an example determines in hourly incrementswhich portion of the controller output (including its capacity), andhence the energy storage asset capacity (including its SOC), can beallocated to the energy and regulation markets respectively, whilemaintaining sufficient reserve to meet the forecasted backuprequirements. The optimal control (to perform the optimization describedherein) can take into account the forecasted and real-time hourly pricesfor each of the markets, along with the time and weather dependentbackup requirements of the facility. When combined with other resourcessuch as renewable generation, backup generation or demand response, theexamples described herein can extract the maximum value of all suchresources while meeting the organization's reliability, comfort, andsustainability objectives.

Following is a description of the different markets, including energymarkets and regulation markets, to illustrate how each market can affectthe operation of an energy storage asset.

Regulation Market

In a non-limiting example, capacity of the energy storage asset may becommitted to the regulation market to maintain the frequency and/orvoltage on the power line. For example, system operators seek tomaintain the system frequency at very near to a nominal frequency ofaround 60 Hz in the U.S. or around 50 Hz in some other countries(including countries in the European Union). If the frequency is toohigh, there is too much power being generated in relation to load. Asystem operator would send a signal to participants in the regulationmarket to increase their load, or ask for generation to be reduced, tokeep the system in balance. If the frequency is too low, then there istoo much load in the system, and the system operator would send a signalasking for generation to be increased or the load reduced. A gridoperator may use a real-time communication signal to call for either apositive correction (referred to in the industry as “regulation up”) ornegative correction (referred to as regulation down”). If load exceedsgeneration, the frequency and voltage tend to drop. The ISO/RTO systemoperator would relay a signal requesting regulation up. If, however,generation exceeds load, the frequency tends to increase. The ISO/RTOsystem operator would relay a signal requesting regulation down(including asking for reduced generation).

The regulation market may seek commitment of a system on an hourlybasis. However, the ISO/RTO system operator may relay regulation signalsfor regulation up and/or regulation down at much shorter timescales. Forexample, during the commitment period, the adjustments of regulation maytake place minute-by-minute, on the order of a minute or a few minutes,or on the order of a few seconds (e.g., at 2-second or 4-secondintervals). Traditional regulation applies to slower responding energystorage assets (e.g., assets with about 5 minutes response time), suchas but not limited to chillers. Faster responding energy storage assets,such as but not limited to batteries, can respond within about 2seconds. In an example, the objective cost function may include a termto performance incentives offered for fast responding energy storageassets. To participate in the regulation market, a resource may receiveand may need to respond to a regulation signal generated by the gridoperator approximately every 2 seconds. (In some territories, this rulemay be relaxed somewhat for batteries.) The energy storage assetresponds to this signal with a percentage of its maximum resourcecapability that is bid into the regulation market. Examples receive andrespond to this signal and distribute it among the various resourcesparticipating in the regulation market within a given price zone, basedon the results produced by an optimizer

If the ISO/RTO system operator sizes the regulation signals toadequately balance the signal in the long run, the charge of the energystorage asset may merely fluctuate around its initial state of chargewhen it started to provide regulation. That is, the proportion of theavailable state of charge of the energy storage asset that is committedfor use to provide regulation may be delivered at variable charge ratesor discharge rates. Adequately balanced regulation signals shouldneither completely deplete nor fill the energy storage asset.

In a non-limiting example, the regulation price may be set at averagevalues of around $30-$45/MW per hour, with hourly rates fluctuatingaround this average value. Some regulation markets may pay simply forthe commitment of an available capacity of the energy storage assetduring a time period, such as for an hour, with a separate payment forthe total amount of energy ultimately provided. Thus, payment at theregulation price may be made for the period of commitment, even if thesystem is not called upon to provide regulation during the commitmentperiod.

There may also be additional payment from the energy market for energygenerated, based on the wholesale electricity market price (the LMP).

Operating characteristics of the energy storage asset include power (orits instantaneous delivery capability in kW) and the energy stored inthe energy storage asset (or the amount of power it can generate overone hour, or kWh). In a non-limiting example, a battery rated at 1.5 MWpower and 1.0 MWh energy storage capacity will be able to provide 1.5 MWpower for a total period of 40 minutes (60×1/1.5). Thus, if the ownerbids 1.5 MW into the regulation market for a given hour, a 50% dischargesignal over 2 seconds could decrease the battery's charge level by 0.8kWh (1.5 MW× 1/1800 hrs).

As part of a certification for participating in the regulation market,the ISO/RTO system operator may verify that the energy storage asset iscapable of responding to the regulation bid into the market. The ISO/RTOsystem operator may require that the energy storage asset be able to becharged/discharged at its full enrolled amount, when receiving a +/−100%regulation signal within a duration of 10 minutes. In the 1.5 MW exampleabove, the battery charge would be increased/decreased by +/−250 kWh(1.5 MW×⅙ hr).

For example, assuming that the energy storage asset starts with aninitial state of charge of 50% at time t=0. Ideally, the regulationsignal is “net zero,” meaning that the quantity of charged/dischargedenergy averages to zero over a given 24-hour period. In reality, thestate of charge of the energy storage asset may at times drift to thelimits of the energy storage asset's recommended state of charge. If thestate of charge exceeds some adjustable maximum or minimum values,various examples include compensating by exiting the regulation marketfor the next hour and bringing the energy storage asset back to itsinitial set-point.

In an example, the operating schedule that is generated according to animplementation of an apparatus herein specifies intervals of time whenthe energy storage asset may be committed to the regulation market.During these time periods, the operating schedule may additionallyindicate the points during these intervals of time where energy may bebought to charge the energy storage asset if its state of charge fallsbelow a desirable limit, or where excess energy may be sold if the stateof charge is too high. This discharge can contribute to a short-termdemand response action in the real-time energy market.

Energy Market

To participate in the energy market, the energy storage asset should tobe able to provide the “as bid” energy into the real-time market for thenext hour. Various examples compute the optimal charge or dischargesignal in anticipation of or in response to the economic signals, whilemaintaining minimum and maximum constraints on the state of charge ofthe energy storage asset. When combined with other controllableresources, such as renewable generation or advanced lighting and HVACsystems, examples extract the maximum economic value of each resource,given external factors and constraints. For example, examples can use anenergy storage asset to compensate for the intermittency of renewablegeneration, and can include demand response actions to help maintain thebalance.

FIG. 11 shows an example energy storage asset optimization in responseto economic signals and performance needs. The horizontal axis is timeover a 24 hour cycle. The left vertical axis is megawatt hours. Theright vertical axis shows price in dollars per megawatt hours. Thevolume under the line battery 810 shows the stored capacity in thebattery. The three lines below the horizontal axis shows the discharge820 from the battery. The seven vertical lines 830 above the horizontalaxis shows charging to the battery 830. The line 840 shows the LMPenergy price throughout the 24-hour cycle to which indicated energyassets are responding. In this example, examples determine the optimizedhourly charge and discharge schedule of a 1.5 MW/1.0 MWh battery inresponse to an LMP price signal. The optimization is further constrainedto maintain a 200 kWh minimum capacity for backup purposes, and amaximum capacity of 800 kWh to maintain charge/discharge cycleefficiency.

Spinning Reserve Market

To participate in the spinning reserve market, the energy storage assetshould to be able to commit resources to provide power during unplannedoutages of base load generators. Spinning reserve is generationcapability that can provide power to the grid immediately when calledupon by the ISO/RTO and reach full capacity within 10 minutes. Theenergy storage asset needs to be electrically synchronized with thegrid, e.g., through the controller, to participate in this market.Revenue in the spinning reserve market is for capacity rather thanenergy. It requires quick response but makes low total energy demand.Requests in the spinning reserve market may be made around 20-50 timesper year.

Revenue for the spinning reserve market may be determined based on theability of an energy storage asset to provide power during an unplannedevent, such as a generator failure. Revenue may also be derived based onthe amount of energy (MWh) that is generated during active participationin the spinning reserve market, such as based on the electricitywholesale price.

Market Based on Voltage/VAR Ancillary Service

To participate in a market based on a voltage/VAR ancillary service,certain resources of the energy asset may be committed to provide forvoltage control and/or VAR control.

The voltage/VAR ancillary service seeks to maintain reliability andpower quality. It may appear at the microgrid level or feeder level of adistribution system.

A voltage control ancillary service assists in maintaining systemvoltages within an acceptable range (120 volts±about 5% or 220volts±about 5%) to customers served by a feeder. For example, if thesupply line voltage fluctuates by some amount, resources of the energyasset may be committed to adjust the distribution primary voltage sothat the distribution primary voltage also does not drift out of theacceptable range. In another example, if the current (ampere) flowing onthe feeder increases during peak load conditions, the voltage along thefeeder may decrease due to an increase in current flow, resulting indecreased voltage for customers that are further from the substation endof the feeder. Here, resources of the energy asset may be committed toraise the line voltage under peak load conditions to account for anyincreased voltage drop. If instead the feeder is lightly loaded, thevoltage drop may be lower, and resources of the transport vehicle may becommitted to lower the voltage to avoid possible high voltageconditions.

VAR refers to the reactive power (measured in volt-ampere reactive(VARs)). VAR is the electrical energy that energizes capacitivecomponents and inductive components in a power system. A non-limitingexample of a capacitive component is overhead conductors, which arecontinuously charged and discharged by an alternating current (AC)waveform. Non-limiting examples of inductive components are electricmotors and transformers, which can store energy in magnetic fields thatare used for device operation. By reducing the amount of VARs flowing onthe distribution feeder, an electricity supplier can reduce electricallosses and improve the voltage profile along the feeder. Where reactivepower varies throughout the day, the capacitive components of a energyasset that are equipped with switches can be placed in or out of serviceas needs vary during the day. These capacitive components of the energyasset may be equipped with controllers. A system, apparatus, or methodmay be used to determine when to switch the switches on or off. Forexample, when the voltage at the location of the capacitive component islow, the operating schedule determined according to a principle hereinmay include instructions for the controller to close the switch to placethe capacitive component in service. When the voltage is high, theoperating schedule may include instructions for the controller to openthe switch to remove the capacitive component from service.

Revenue from a market based on the voltage/VAR ancillary service may bedetermined based on the ability of an energy storage asset of the energyasset(s) to be used to provide the voltage controls and/or the VARcontrols. In an example, the voltage/VAR control may apply in amicrogrid application at the microgrid bus level, which may introduce areliability cost to the computation of the net-energy-related cost.

Co-Optimization Across Multiple Markets and/or Ancillary Services

As described above, the economic signal can be a driver for the averagecharge status of the energy storage asset. It responds to price signalsthat are averaged on an hourly basis. The regulation signal can be seenas having a “bias” effect over the average charge, in response to theregulation commands. Examples co-optimize the energy storage assetcharge by first economically optimizing the charge status of the energystorage asset, then allocating the balance of the available power to theregulation market, on an hourly basis.

By adding user-adjustable upper and lower constraints to the optimizedenergy storage asset charge, examples take into account reliabilityobjectives (e.g. backup) and charge/discharge cycle efficiency. Otherconstraints can be added, based on the type of energy storage assettechnology used, to maximize charge/discharge round trip efficiency, andoptimize energy storage asset life versus energy storage assetreplacement costs.

In addition to co-optimizing a storage resource at a given location,examples have the capability to perform a global optimization acrossmultiple customers within the same price zone, and disaggregate theregulation and economic signals among the various customers. Inparticular, this gives customers that do not have the minimum energystorage asset capacity required the ability to participate in theregulation market.

Co-Optimization with Other Distributed Resources

With various examples, distributed resources can earn maximum economicbenefit through co-optimization. Co-optimization of various resources onone site results in accelerated payback for all assets, and this, inturn, accelerates the market-wide penetration of these resources.

FIG. 12 shows an example generation schedule for battery-photovoltaicco-optimization. FIG. 12 shows an example where the same battery used inthe previous example in FIG. 11 is combined with 0.5 MW of PV(solar-photovoltaic) generation. The horizontal axis shows the time inthe 24-hour cycle. The left vertical axis shows megawatt hours. Theright vertical axis shows price in dollars per megawatt hours. The load910 is the electric load on the facilities. The import of power 920shows the power imported into the facilities from the grid. The battery930 shows the three bars below the horizontal axis for the powerdischarge from the batteries at specific times. The diesel 940 is notshown because diesel generation is not used in this co-optimizationbecause of its relative price. The solar 950 shows the power used by thesystem and/or stored in the batteries from the solar generator orphotovoltaic generator at various times. The LMP line 960 shows thefluctuating price for electricity during the 24-hour cycle.

Example Energy Storage Assets

Various examples are technology agnostic and can optimize any storageinstallation. However, certain forms of storage, such as compressed airand ice storage, are currently not recognized as applicable resourcesfor some regulation markets.

Aided by significant private investment, grid-scale batteries havesignificantly reduced in cost over the past decade. Differenttechnologies appear to have converged around a similar price: withbatteries offered at roughly $1-2 per Watt, and $1-2 per Watt-hour,before Balance of Plant (“BoP”) costs. (Watts [W, kW, MW] are a measureof power, i.e., the charge and discharge rate of an energy storageasset. Watt-hours [Wh, kWh, MWh] are a measure of energy, i.e., thestorage capacity of an energy storage asset.) At these prices, energystorage asset owners and lessees can use examples to achieve a positivereturn over the installed life while meeting their sites' backup needs.

Below is a brief overview of each different types of energy storageassets:

Lithium-Ion Battery

This “power battery” is well-suited for regulation with high efficiencyand hybrid opportunities. However, it has a high cost and little dataexists to corroborate lifespan claims

Quoted prices include $2 million for a 1 MW/1 MWh unit, and $1.5 millionfor a 1 MW/250 kWh unit.

Lithium-Ion (Li-Ion) batteries are receiving great attention becausethey are the preferred battery for electric vehicles. Presently, Li-Ionbatteries are among the most expensive of the storage options available.This may change, as many companies are pouring resources into new Li-Ionvariants; however, some suggest that the chemical characteristics ofLi-Ion cells make it difficult to significantly reduce their cost.Additionally, Li-Ion is a new technology so that no company hasempirically demonstrated Li-Ion's lifespan. Companies have tried toallay these concerns through “accelerated testing” that charge/dischargethe battery more rapidly, but this does not provide full insight intohow well Li-Ion batteries perform over time.

Li-Ion batteries are very dense and therefore very small compared toother technologies. One manufacturer's 1 MW/1 MWh unit, for example, hasdimensions of 8′×20′. In comparison, a quoted lead-acid unit withsimilar specs has dimensions of 40′×70′.

Hybrid opportunities for Li-Ion batteries are discussed in the flowbattery section.

Lead-Acid Battery

This battery is the lowest-cost option with long lifespan and proventechnology. However, it is physically large with high maintenance andlimited depth of discharge.

Quoted prices include $896,000 for a 1 MW/2 MWh unit, and $512,000 for a1 MW/500 kWh unit.

Lead-Acid batteries, which have the same chemistry as a car battery, areproven for long-lasting grid applications. One manufacturer's 1 MW/1.4MWh unit lasted for 12 years, from 1996-2008, as both a provider ofvoltage support and a backup power source, before the battery cells werereplaced. The original power electronics of that installation stillfunction, and the unit is running with a new set of lead-acid cells.

A downside of lead-acid batteries is that they are very heavy and verylarge. This is why they are not being considered as much for EVs, andthis poses other logistical challenges for metropolitan installations.Lead-acid batteries are also considered to be high maintenance. Theyneed to be kept within a narrow temperature range, and therefore requiretheir own building (for industrial power uses), as well as periodicupkeep. Also, lead-acid batteries are typically oversized becauseexceeding the lower bounds of their state of charge can damage thecells. They are best for regulation or voltage support, and as backup ifsized explicitly for that purpose.

Flow Batteries

These batteries can be fully charged and discharged without damage tothe battery. Also, “hybridization” is possible. However, this “energybattery” limits regulation market opportunities and has low round-tripefficiency.

Quoted prices include $1.15 million for a 1 MW/1 MWh battery.

Flow batteries are energy batteries, i.e., they are best suited forbackup electricity, but their chemistry limits their ability to providehigh-MW regulation. The typically configured flow battery takes 4 hoursto charge/discharge, and flow batteries have lower round-tripefficiencies than other types (roughly 75% in contrast to Li-Ion's 90%).With flow batteries, a tank is filled with electrolyte fluid that flowsthrough solid cell stacks located at the top of the unit. The liquidsolution never degrades, but the cells need to be replaced every 5 or 6years. The cost of cell replacement is 10-15% of the total unit.

The electrochemical characteristics prohibit them from power-denseapplications, unless they are oversized and paired with a largeinverter, or “hybridized” with another battery technology. Hybridizationcan be provided by some suppliers in conjunction with a well-establishedpower electronics provider. One manufacturer has created a system thatallows its “energy” batteries to be paired with “power” batteries, likelithium-ion, connected through a single inverter. A leading lithium-ionbattery manufacturer recently announced a plan to provide a similarLi-Ion/flow battery unit for grid-scale applications.

Dry Cell Technology

This power battery is good for the regulation market. However, it hasvery small recommended depth of charge/discharge and is expensive.

Quoted prices include $1.5 million for a 1.5 MW/1 MWh battery, plus 30%extra for BoP (“Balance of Plant”).

These batteries provide high power-to-energy ratios that make themattractive for regulation, so long as they remain within a fairly narrowrange of state of charge. These batteries are not meant to fully chargeor discharge and pushing their recommended operating parameters affectstheir lifespan. Ideal state of charge is 20-80%. Because of theseconstraints, these batteries would need to be oversized to providebackup. These batteries are more expensive than cheaper options such aslead-acid.

Based on their characteristics, these batteries are likely suited forprojects whose primary objective is not backup power, but rather systemssupport. They provide high-MW regulation, can address voltage sagconcerns, and can be recharged by regenerative braking However, whentheir state of charge limitations are taken into account, they appear tobe a costly technology, even in comparison to lithium-ion.

Ice Units

The thermal storage capacity of an ice unit can be used according to theprinciples herein as an energy storage asset.

Ice units can be used to modify how a transport vehicle is cooled,including how energy is consumed for cooling/air conditioning. An iceunit generally consists of a thermally-insulated storage tank thatattaches to a transport vehicle's air-conditioning system. The unitmakes ice (generally at night when supply costs tend to be lower) anduses that ice during the day to deliver cooling directly to thetransport vehicle's existing air conditioning system. Storage tanks canbe on the order of hundreds of gallons of water (e.g., about 450gallons) of water. The water is frozen by circulating refrigerantthrough copper coils within or surrounding the tank. The condensing unitthen turns off, and the ice is stored until its cooling energy isneeded. During the higher temperature daytime hours, the powerconsumption of air conditioning and demand levels on the grid, increase.The ice unit may be used to replaces the energy-demanding compressor ofa transport vehicle's air conditioning unit. The melting ice of the iceunit, rather than the air conditioning unit, can be piped around thetransport vehicle to cool it.

Compressed Air

The storage capacity of compressed air can be used according to theprinciples herein as an energy storage asset.

For example, compressed air energy storage (CAES) technology provides away to store compressed air, using energy generated at lower cost at onetime, and use that compressed air at another time when energy costs arehigher. For example, energy generated during periods of low energydemand periods (such as during off-peak electricity usage as night) maybe released at on-peak times to meet higher demand. The CAES system maybe located where there is large, accessible air-storage pockets orcaverns, such as but not limited to mines and underground formations.The air may be compressed using electrically powered turbo-compressors.The compressed air stored in these pockets may be later fed to, e.g.,gas-fired turbine generators to generate electricity during on-peak,higher-priced time periods. In another example, the compressed air isexpanded using turbo expanders or air engines that are drivingelectrical generators to generate electricity.

In another example, the thermal storage capacity of compressed air canbe used according to the principles herein as an energy storage asset.

Using a heat exchanger, it is possible to extract waste heat from thelubricant coolers used in types of compressors, and use the waste heatto produce hot water. Depending on its design, a heat exchanger canproduce non-potable or potable water. When hot water is not required,the lubricant can be routed to the standard components for lubricantcooling. The hot water can be used in central heating or boiler systems,or any other application where hot water is required. Heat exchangersalso offer an opportunity to produce hot air and hot water, and allowthe operator some flexibility to vary the hot air to hot water ratio.

Controller for an Energy Storage Asset

The controllers for the energy storage assets described herein can beused to vary the input to or output from the energy storage assets. Whenthe controller functions as a converter, it converts the AC signal to aDC signal. That DC signal may be used to charge the energy storageasset. When the controller functions as an inverter, it converts onetype of voltage (direct current (DC)) into another type of voltage(alternating current (AC)). Since the electricity supplier generallysupplies 110 or 220 volts AC on the grid, the conversion may typicallybe from 12 volts DC to 110 or 220 volts AC. In another example, theoutput of the controller may be different, depending on the type of loadon the system. Inverters called utility intertie or grid tie may connectto energy generating assets such as solar panels or wind generator, andcan feed their output directly into the inverter. The inverter outputcan be tied to the grid power.

In a non-limiting example, the inverter takes the DC output from theenergy storage asset and runs it into a number of power switchingtransistors. These transistors are switched on and off to feed oppositesides of a transformer, causing the transformer to think it is gettingan AC signal. Depending on the quality and complexity of the inverter,it may put out a square wave, a “quasi-sine” (sometimes called modifiedsine) wave, or a true sine wave. The quality of the quasi-sine wave canvary among different inverters, and also may vary somewhat with theload.

The virtual partitioning of the energy storage asset describedfacilitates partitioning between energy and regulation participation.The partitioning can be based on the available capacity of thecontroller (i.e., the inverter/converter). The SOC of the energy storageasset may be used to provide a constraint within the optimization fordetermining the optimal charge/discharge strategy for participation inthese two different markets. As a non-limiting example, an operatingschedule generated according to the principles herein can indicate theoptimal charge/discharge strategy for the controller, including on anhourly basis, in response to or anticipation of projected LMPs. Thebalance of the inverter capacity of the controller may be made availableto the regulation market at its shorter timescales (e.g., at the2-second or minute-by-minute time intervals described above). Theproportion of the controller output (and hence the energy storage asset)committed to the energy market and the remaining proportion of theenergy storage asset committed to the regulation market are co-optimizedbased on the economic benefit derived from the two markets, and subjectto the SOC constraints. The operating schedules generated based on anyof the principles described herein, and in any of the example, cansuggest the proportion of the controller output committed to the energymarket and to the regulation market in a given time interval t (lessthan time period T), and for what length of time. the proportion of thecontroller output committed to the energy market and to the regulationmarket in a given time interval t (less than time period T). Forexample, for a controller with a 1 MWatt inverter capacity, theprinciples herein can be used to generate an operating schedule thatsuggests the proportion of the controller's 1 MWatt inverter capacitythat can be committed to the energy market and to the regulation marketin a given time interval t to generate the energy-related revenue.

Energy Generating Assets

Examples of energy generating asset applicable to the apparatus andmethods herein include photovoltaic cells, fuel cells, gas turbines,diesel generators, flywheels, electric vehicles and wind turbines.

Electric storage has the potential to address some of the attributes ofrenewable energy generation. The intermittent nature of energygenerating assets, including solar generation, may present somedifficulty for grid operators. For example, weather events can makeenergy output of energy generating assets, including photovoltaic cellsor wind turbines, difficult to predict. As renewable generators make upa growing share of regional generation portfolios, grid operators mayrequire greater real-time visibility of distributed generation andbenefit from a resource's ability to control bi-directional power flow.Adding storage to distributed generation achieves new levels ofresponsiveness not seen with existing systems.

According to principles described herein, the operating schedulegenerated for a system that includes a controller, an energy storageasset and an energy generating asset can firm up intermittent renewablegeneration into dispatchable generation. The operating schedule canprovide for renewable generation forecasting based on the forecastedweather conditions.

Dynamic virtualization can be beneficial to sites that utilize bothenergy storage assets and energy generating assets. For example, byintegrating weather data, price forecasts, and expected site load,examples can accurately predict a solar array's output, determine howmuch solar generation should be captured by an energy storage asset, anddispatch the energy storage asset at the time of day that optimizesrevenues derived from wholesale market participation.

By passing energy through an energy storage asset and exhibitingreal-time control, power can be delivered strategically and act as aprice-responsive resource in the various wholesale markets. In effect,storage allows the maturation of energy generating assets as a resourcethat provides discrete power-flow to the grid that is controllable,quantifiable, and dispatchable. Solar power and its generation can becostly. Through dynamic virtualization the value of renewable generationcan be increased by improving the resource with electric storage.

Regenerative Braking Energy from Transportation Systems and CarriageSystems

An example implementation of a system, apparatus and method herein isdescribed based on a carriage system, such as but not limited to anelevator system or other similar system based on a platform orcompartment housed in a shaft for transporting people or things todifferent vertical levels or horizontal positions. For example, thecarriage system can be used to raise and lower people or things todifferent floors or levels of a building, a subterranean system, or movethe people or things through other types of multi-level or multi-stagesystem. In this example implementation, the regenerative braking energyis generated through the slowing down (braking) of a cable, pulley, orother similar device that facilitates the movement of the platform(s) orcompartment(s) of the carriage system. At least one energy storage assetmay be in electrical communication with the carriage system to capturethe regenerative braking energy generated during at least one brakingevent during the movement of the platform(s) or compartment(s) of thecarriage system.

An example implementation of a system, apparatus and method herein isdescribed based on a substation of a transportation system, such as butnot limited to an electric railway power traction system. At least oneenergy storage asset may be in electrical communication with at leastone railroad car of a train of the transportation system, to capture theregenerative braking energy generated during at least one braking eventduring the movement of the train. A train (or train system) hereinrefers to at least one railroad car pulled or pushed by one or morelocomotives or power cars. The train or train system can include aseries or plurality of railroad cars, which may be connected. Thetransportation system may include a ground level train system, anelevated train system, a subterranean train system, or any combinationof the three. For example a train running on a single line in a railwaysystem may travel in all three types of modes. In an example, thetransportation system includes a trolley line or a tramway system. Theexample system, apparatus and method herein can be implemented toprovide energy savings to an energy customer based on the capture ofregenerative braking energy from at least one electric train beingserviced from a substation. An example system, apparatus and methoddescribed herein can facilitate participation in the wholesale energymarket as a resource for demand response.

An example system, apparatus and method according to the principlesherein can be implemented to provide energy savings by capturing theregenerative braking energy produced by the braking of the electrictrains of a transportation system or a carriage system using at leastone energy storage asset, and using the energy to participate in awholesale energy market and/or a regulation market as a resource fordemand response. The at least one energy storage asset can be, but isnot limited to, at least one wayside energy storage asset.

According to the principles herein, the wayside energy storage assetsare located at the substations of transportation systems, or at thepower units for the carriage systems, according to the exampleimplementation.

In another example implementation, an aggregation of electric vehiclesand/or hybrid electric vehicles that are in communication with at leastone energy generating asset can be committed to a regulation marketand/or an energy market as energy storage assets to facilitate derivingenergy-related revenue. For example, the parking lot of a train stationcan include multiple coupling units to which electric vehicles and/orhybrid electric vehicles can be coupled so that their on-board energystorage units can be charged while the electric vehicles and/or hybridelectric vehicles remains coupled to the coupling unit. The on-boardenergy storage units of the electric vehicles and/or hybrid electricvehicles can be, but is not limited to, batteries and/or fuel cells.Non-limiting examples of batteries include a lithium ion battery, a leadacid battery, a flow battery, or a dry cell battery. The lot can includeenergy generating assets, such as but not limited to photovoltaic cells,wind generators, or diesel generators, that are coupled to the multiplecoupling units for charging the electric vehicles and/or hybrid electricvehicles. The coupling unit(s), and/or the electric vehicles or thehybrid electric vehicles, can include at least one controller that canbe used to control the charging/discharging of the energy storage units.According to an example implementation, the charge capacity andcharge/discharge rates of the multiple electric vehicles and/or hybridelectric vehicles in the lot can be aggregated to provide an aggregateenergy generating capacity and an aggregate charge/discharge capacitythat may be committed to the regulation market and/or the energy market.The cumulative charge from the energy generating assets can be stored tothe electric vehicles and/or hybrid electric vehicles and hencecontribute to the aggregate energy generating capacity of the electricvehicles and/or hybrid electric vehicles. For example, an apparatusherein can be used to generate an operating schedule for thecontroller(s) of the coupling unit(s) and/or the electric vehicles orthe hybrid electric vehicles such that energy-related revenue may bederived based on the participation of the aggregate energy generatingcapacity and the aggregate charge/discharge capacity of the electricvehicles and/or hybrid electric vehicles. In this exampleimplementation, the operating schedule for the controller of theaggregation of electric vehicles and/or hybrid electric vehicles asenergy storage asset can be determined (e.g., using an optimizer module)based at least in part on an operation characteristic of the aggregationof electric vehicles and/or hybrid electric vehicles, anenergy-generating capacity of at least one energy generating asset incommunication with the at least one energy storage asset, and aregulation price and/or a forecast wholesale electricity priceassociated with the respective market.

As with other systems and apparatus described herein, the netenergy-related cost for this example implementation can be computedaccording to any of the methods described herein. As a non-limitingexample, the net energy-related cost can be specified as a differencebetween an electricity supply cost and an economic demand responserevenue over a time period (T). A mathematical model that can be used tofacilitates a determination of the operating schedule for the controllerof the at least one energy storage asset can also take as input areplacement cost for the at least one energy storage asset, aconvenience cost, and/or any other applicable costs described herein.For example, the net energy-related cost can include a term that relatesto the replacement cost, convenience cost, and/or any other applicablecosts described herein.

In another example implementation, the wayside energy storage asset(s)can be coupled with the aggregation of electric vehicles and/or hybridelectric vehicles, and the combined capacity can be committed to theregulation market and/or the energy market as energy storage assets tofacilitate deriving the energy-related revenue. As described herein, thewayside energy storage asset is charged using excess regenerativebraking energy from the braking motion of the trains of thetransportation system according to the principles herein. The operatingschedule can be generated to operate the controller of the couplingunit(s) and/or the electric vehicles or the hybrid electric vehicles, aswell as the controller of the wayside energy storage unit, according tooperation schedules that could facilitate deriving energy-relatedrevenue. According to an example implementation, the charge capacity ofthe multiple electric vehicles and/or hybrid electric vehicles in thelot can be aggregated with the capacity of the wayside energy storageasset to provide an aggregate energy generating capacity and anaggregate charge/discharge capacity that may be committed to theregulation market and/or the energy market. For example, an exampleapparatus herein can be used to generate an operating schedule for thecontroller(s) of the coupling unit(s) and/or the electric vehicles orthe hybrid electric vehicles such that energy-related revenue may bederived based on the participation of the aggregate energy generatingcapacity and the aggregate charge/discharge capacity of the electricvehicles and/or hybrid electric vehicles.

An example system, apparatus and method implemented according to theprinciples herein can provide an energy customer with significant costsavings and environmental benefits by using less electricity andreceiving revenues from an operator in the wholesale electricity market.The example system, apparatus and method also can be implemented toexpand the range of use of an energy customer's existing regenerativebraking and energy capture system by storing the energy in energystorage assets, where it can be used for additional purposes. Theexample system, apparatus and method also can be implemented to optimizethe power and voltage quality of an energy customer's system, e.g., byproviding carefully controlled and timed injections of power into asubstation or other system that interfaces with an electricitytransmission system. In an example, the captured electrical energy fromthe regenerative braking can be used in place of purchased energy forcertain applications (e.g., purchased energy from an electrical powertransmission, a power grid, or a microgrid). Such use can provideenvironmental benefits.

In an example implementation, the captured regenerative braking energy,whether from a carriage system or a transportation system, can be usedto provide an operator in a regulation market with demand response orenergy and regulation/ancillary resources, by optimizing the carriagesystem's or the transportation system's use of the captured energy andby using less electricity during higher cost hours (e.g., peak usagehours). The regenerative braking energy that is captured in the energystorage asset can be committed to an energy market and/or a regulationmarket based on an operating schedule that is generated according to asystem, apparatus or method described herein.

An operating schedule, or any other output of an implementation of asystem, apparatus and method herein, can be used by an operator in anenergy market and/or an operator in a regulation market to determine anearnings compensation (a revenue) for the energy customer.

In an example implementation, any excess stored energy in the energystorage asset(s) can be committed for balancing electric generation andelectric load on a regulation interconnection system, which can assistan energy customer to stabilize its electric distribution system. Anexample system or apparatus herein can be used to develop, analyze anddisseminate additional data about the benefits of energy storage andoptimization. An example system or apparatus herein also can be used ocreate a replicable and scalable model for energy savings and poweroptimization that can be deployed more broadly on a transportationsystem or a carriage system.

In an example implementation, a system, apparatus and method herein canbe used to determine an operating schedule that includes time intervalsfor regenerative energy capture. The example system or apparatuscaptures any regenerative braking energy that is in excess on a tractionpower line of a transportation system or other similar power line of acarriage system. The example system or apparatus can be operatedaccording to an operating schedule to automatically detect this excessenergy by apply an algorithm (and associated method) that identifies theelectric signature of the regenerative braking. The amount of energycapture may be proportional to the amount excess energy available forcapture and/or the state of charge of the energy storage assets, such asbut not limited to a battery bank. For example, the system can beprevented from capturing energy from any rectifiers. The example systemor apparatus can no longer capture regenerative braking energy if theenergy storage asset is already fully charged.

In an example implementation, a system, apparatus and method herein canbe used to determine an operating schedule that includes time intervalsfor regenerative energy return. For example, if the voltage on atraction power line of a transportation system or other similar powerline of a carriage system drops below a certain threshold value, e.g.,when a train or elevator car starts, an example system or apparatusherein can be operated according to an operating schedule to provideenergy from the energy storage assets to support the voltage and bringit back into its normal operating range. The transportation system orcarriage system may be configured such that this functionality isautomatically disabled when the voltage on the traction power line orother similar power line is very low, e.g., if any rectifiers aredisconnected for maintenance, and/or for safety reasons (e.g., if thereis an emergency power shutdown.

In an example implementation, a system, apparatus and method herein canbe used to determine an operating schedule that includes time intervalsfor the energy storage asset(s) to be used for frequency regulation,based on an operating schedule that specifies a suggested charge anddischarge of the at least one energy asset: The example system is ableto respond to charge and discharge orders when required by a module thatperforms the optimization to provide the operating schedule. Both chargeand discharge may be modeled as proportional to the value of the systemoperating point (such as a set point). Controlling signals from anoperator in the frequency regulation market can be sent to the modulethat performs the optimization.

In an example implementation, a system, apparatus and method hereinfacilitates real-time computation of an energy-related revenue based onan operating schedule that specifies a suggested charge and discharge ofthe at least one energy asset: The system is able to respond to thecommands during implementation of the operating schedule (of charge anddischarge periods). Both charge and discharge may be modeled asproportional to the value of the system operating point (such as a setpoint).

An example system, apparatus and method herein can be used to determinean operating schedule that provides for energy flow control andoptimization. An example system or apparatus integrates the properalgorithms (and associated method) applied by a module to optimize theefficiency of the energy flows between the energy storage asset(s) and agrid, transmission line or microgrid.

An example system, apparatus and method herein can be used for energymetering and data logging. The energy flows between the energy storageasset(s) and a grid, transmission line or microgrid, should be meteredaccording to industry requirements. The data flows can be recorded inreal-time on a memory (such as but not limited to a non-volatile memory)by a power control system. As an example, the data flows can be recordedon a memory with an accuracy of about 0.5 seconds. The example apparatusor system can be configured such that the data is viewable, retrievableand exportable in different formats, such as but not limited to either acomma-separated-value (CSV) file format or a spreadsheet file format.This can be accomplished through a secure user interface. In an example,the user interface to the power control system can be a web-based userinterface that is accessible both locally and remotely. In an example,the user interface is a portal to a power control system based on asoftware-as-a-service model.

An example system, apparatus and method herein can include at least onecommunication interface and control. The example system or apparatus caninclude interfaces to integrate the control ports of the various piecesof equipment, including a supervisory control and data acquisition(SCADA) interface. SCADA is a non-limiting example of a type ofindustrial control system. The example system, apparatus and method canused a communication protocol that is a standard, industrial protocol.An example power control system according to the principles herein mayhave a local port (e.g., an Ethernet) and/or a remote access (e.g., IP)to monitor the example system or apparatus and adjust its parameters.

The example transportation system or carriage system can includeprotective measures such that the equipment is protected from theeffects of transients from the traction power line of a transportationsystem or other similar power line of a carriage system.

FIG. 13 shows a block diagram overview of a portion of an electric railsystem including one or more energy storage assets (e.g., batteries), apower control system (PCS) for controlling the one or more energystorage assets, and a scheduling apparatus for determining operatingschedules (e.g., charge/discharge schedules and/or rates ofcharging/discharging) for the one or more energy storage assets,according to an example. As shown in FIG. 13, the scheduling system canbe configured to communicate with the PCS. The PCS can be configured tocommunicate with at least one of an analog-to-digital (A to D)converter, a RTU, a DC-to-DC converter, and an energy management systemfor the energy storage asset (in this example, a battery managementsystem (BMS) for batteries). For example, the communication can bethrough a Modbus/IP (the Modbus RTU protocol with an IP networkingstandard that runs on the Ethernet. The RTU is an electronic device thatis controlled by a microprocessor, and is used transmit data to SCADAsystems. As also shown in FIG. 13, the scheduling apparatus can beconfigured to communicate with the market operator via a communicationgateway (such as but not limited to ARCOM). FIG. 13 also shows the linesfeeding from the electricity grid to the electrical lines of thetransportation system. The battery stack shown in FIG. 13 can be part ofa wayside energy storage asset system. The communication between theenergy storage asset(s) (the battery stack) and the lines that feed tothe rails is shown at “B”. The transport vehicles (not shown) are inelectrical communication with the 3^(rd) rail and/or the surfacetrolley. The regenerative energy from the braking motion of thetransport vehicles can be used to charge the energy storage asset(s),e.g., via the communication pathway at “B”. While the lines of thetransportation system are shown at current values of 4000 A and 2000 A,the apparatus, systems and methods herein are not limited to such atransportation system.

FIG. 14 shows an example method for generating energy-related revenue inconnection with operation of an electric rail system. The electric railsystem includes at least one energy storage asset to store regenerativebreaking energy arising from the operation of the electric rail system.The method includes electronically determining a suggested operatingschedule for charging and discharging of an energy storage asset basedon optimization of an objective cost function representing, at least inpart, a demand response revenue from a regulation market and/or awholesale electricity market, and controlling the energy storage assetaccording to the suggested operating schedule. The method of example 14can be implemented using the system shown in FIG. 13.

FIG. 15 shows an example implementation that includes an examplescheduling apparatus, energy storage asset(s), power grid and transportvehicles of an electric railway system. The arrows in FIG. 15 shows thevarious communication links between the components. It should beunderstood that the type of communication varies depending on thecomponents involved. For example, the communication pathway between thepower grid and the energy storage assets is an electrical pathway can bean electrical pathway, while the communication between the schedulingapparatus and the energy storage assets can be via a communicationprotocol, i.e., data or other information representing the operatingschedule is transmitted to the controller of the energy storageasset(s). It should be understood also the communication between thecomponents can be direct or indirect (i.e., through at least oneintermediate component).

Transportation System

An example transportation system that can be used in a regulation marketcan be configured to be able to store or return energy during a minimumof 15 minutes at 500 kW. For the regenerative braking energy, eachmarried pair of a train system can regenerate a current of about 2750 Aor less during braking. This current is reduced because the voltage islimited to 800 VDC maximum (‘clipping’). Some of this energy can be usedfor a train car's auxiliary components, including compressors, HVACs,lighting) and some is absorbed by the natural receptivity of thetraction power line. The amount of energy to be captured by the energystorage asset(s) of the transportation system is estimated at about 3.6kWh per coupled pair; i.e., 10.8 kWh per 6-car train. A DC-to-DCconverter of the transportation system can be rated to 1.5 MW incontinuous use. In order to maximize the capture of regenerative energy,the converter is able to operate at twice its nominal rating for amaximum of 30 seconds every 2 minutes during normal operating hours. Dueto its power rating, the DC-to-DC Converter may limit the amount ofenergy to be captured during a regenerative braking event.

FIG. 16 shows an example of an average weekday load (in units of kWh)versus time for a transportation system. The values of load can be aninput to the apparatus described herein for generating an operatingschedule for a controller. In an example, the operating schedule isoptimized such that the load requirements of the transportation systemare met at a given time t by power from a grid, power from the energystorage asset (which is being charged using the regenerative energy), orany combination of the two.

Energy Capture Computation:

The generation profile of the regenerative braking energy can betriangular-shape. The power rises with the start of the braking event,to a peak in value at approximately 1 second, then decrease linearlywhile the train slows down. The generated power ends abruptly when themechanical brakes are applied, just before stopping. Therefore, thetotal energy can be estimated by this formula:

E=(P _(peak)×Duration)/2

where P_(peak) is the peak power. The peak regenerative braking powerfor a coupled pair going at 49 mph is

P _(peak)=(3900 A×1390 V)=5.4 MW.

The maximum voltage allowed by the infrastructure of is 800 V. Assumingthe load is linear, the maximum power can be expressed as:

P _(peak) at 800 v=(2250 A×800 V)=1.8 MW.

Each train of the transportation system includes three (3) coupled pairof railway cars, so the total peak power during regeneration can beexpressed as:

P _(peak) _(_)train=(3×1.8 MW)=5.4 MW.

The total regenerative energy at 49 mph (braking time 15 sec approx.)can be expressed as:

E=5.4 MW×15 s/2=40.5 MJ or 11.25 kWh

Assuming that the auxiliaries of the railway car and the naturalreceptivity of the line capture immediately 40% of the regenerativeenergy, the remainder is:

E=11.25×60%=6.75 kWh

A DC-to-DC Converter used in a transportation system can have a peakrating of about 2.25 MW. The maximum energy handled in 15 seconds can beexpressed as 2.25 MW×15 s=22 MJ or 9.37 kWh. Due to the triangle-shapedenergy wave, this result is divided by 2 (that is 4.7 kWh).An assumption can be made that the train speed is about 50 mph, themaximum voltage is about 800 V, the braking time is about 15 seconds,and the natural line receptivity and auxiliary load of about 40%. Therecoverable energy per braking event can be computed at about 4.7 kWh.

System Components

An example system according to the principles herein can include atleast one energy storage asset. As a non-limiting example, the energystorage assets can include one or more batteries. For example, in atransportation system application, batteries that have an estimatednominal capacity of about 1 MWh may be used. The usable capacity of sucha battery may be about 700 kWh (i.e., 70% of its nominal capacity) witha 1.5 MW permanent power rating (charge and discharge). As anon-limiting example, the energy storage assets can include one or morelithium-ion batteries having a capacity of about 400 kWh, with a powerof about 1.1 MW continuous, a peak power of about 1.5 MW (for about 15to about 30 seconds). In an example the lithium-ion batteries can be alithium nickel cobalt aluminum oxide (NCA) battery or a lithium titanatebattery. The energy storage assets can be designed to minimizemaintenance. If an energy storage asset requires protection from itsenvironment (including dust, temperature, humidity, etc.),specifications may be determined for the form of the protectionequipment (including cabinets to enclose the energy storage asset,methods to cool the energy storage asset, sizing of the energy storageasset).

An example system according to the principles herein can include anenergy asset management system (EMS). In an example where the energystorage asset(s) includes a battery, the EMS can be a Battery ManagementSystem (BMS). For example, a BMS can be used to manage the state ofcharge of the battery(ies), the status of the battery(ies), thesignaling of an alarm, and provide an interface to a power controlsystem (PCS). The EMS can be used to monitor the energy storage asset(s)and communicate in real-time its status to the power control system. TheEMS also can be configured to integrate safety and alarm features toallow the other components of the system to perform any regular oremergency start-up and shut-down safely.

The example system can include a power control system (PCS). The PCS caninclude machine readable instructions to apply an optimization algorithmto the input parameters. The PCS can also include a user interface (suchas a portal to a software-as-a-service platform), a SCADA interface, aninterface to an EMS (such as but not limited to a BMS), an interface toan example apparatus or system herein that includes the optimizermodule, an interface to a DC-to-DC converter. The user interface can beconfigured to provide local access and/or remote access to a user.

The example apparatus that includes the optimizer module can beconfigured to interface with the PCS, the SCADA system (to providestatus, alarms, metering, etc.), the feeds of the LMP (forecast, actualfeeds, or any combination thereof), and/or the system of the operator inthe regulation market (through which signals can be sent forparticipation in a regulation market).

In an example where the energy storage asset is at least one battery,the system can include a DC-to-DC converter. The DC-to-DC converter isbi-directional and can present the same rating in either direction. Asan example, a DC-to-DC converter can have a capacity of around 1.1 MWnominal and about 2.25 MW at peak power. As another example, a DC-to-DCconverter can be rated 1.5 MW (permanent) in both directions. Such aDC-to-DC converter can operate between 500 VDC and 800 VDC on both thetraction power side and the battery bank side. The battery bank may notbe referenced to the ground, but the negative of the traction power maybe grounded or nearly grounded (a few ohms) A DC-to-DC converter can beused to sustain transients from traction power equipment without damage.In an example, such a DC-to-DC converter can be operated to 3 MW atregular intervals, such as but not limited to for 30 seconds every 2minutes, in order to maximize the regenerative braking energyrecuperation. The DC-to-DC converter can be disposed in communicationwith the interface to the PCS and the energy storage asset(s).

An example PCS can be configured to integrate the various components inorder to operate the system according to its specifications. The examplePCS can be configured to communicate with the other components of thesystem. For example, the PCS can be configured to coordinate theoperation of an example apparatus for generating the operating schedule,a DC-to-DC converter, and/or an EMS. In an example, the PCS also can beconfigured to control the DC-to-DC converter in real-time to performvarious functions, including the operation of the energy storage assetto perform the operating schedule generated according to a methodherein. Non-limiting examples of such functions include charging theenergy storage asset(s) using the regenerative charge generated from abraking motion of the transport vehicle or the carriage compartment orperforming the economic charge or discharge of the energy storage asset,including according to a co-optimization or to a dynamic partitioning ofthe energy storage asset. The PCS can be used to monitor and log into amemory the energy mitigated through the DC-to-DC converter. The log filecan be formatted in CSV or XLS format. The operational parameters can bemade accessible and programmable through the user interface. Forexample, a log file of the energy mitigated through the converter can bemade retrievable through the user interface.

An example apparatus or system herein can include a module that monitorsa traction power substation, including providing metering, indicationsof status, and alarms.

An example apparatus or system herein can include an ARCOM interface.The ARCOM interface is an industrial communications gateway that can beused with the energy storage asset(s) to receive a signal from anoperator in a regulation market and provide signals to control thecharging and discharging of the energy storage asset(s).

An example apparatus or system herein can include traction powercomponents installed at a substation of a transportation system.Non-limiting examples of the traction power components include rectifiertransformers, rectifiers, switchgear, protection relays, and the SCADAinterface of a traction power substation (TPSS).

Transportation System Substation

Any example system, method, or apparatus according to the principlesherein can be integrated with the infrastructure of a transportationsystem. An operating schedule generated according to the principles ofany of the example system, method, or apparatus herein can beimplemented using the transportation system's infrastructure.

Control and Optimization

An example apparatus or system according to the principles herein caninclude a module for generating an optimized operating schedule for thecontroller of the energy storage asset(s). For example, the optimizationcan be performed to maximize the total savings for the energy customer.These savings can include supply savings, and maximizing revenue fromthe regulation energy (economic) markets and/or the regulation market.The example apparatus and system can be used to perform the optimizationbased on the LMP forecast and actual feeds. In another example, theapparatus and system can be used to perform the optimization based onthe LMP forecast and on other parameter, such as but not limited to aweather forecast and/or metering inputs from the substation.

Once a schedule is generated, an example apparatus or system describedherein can be implemented to transmit the operating schedule to the PCSas one or more command signals. For example, an apparatus or systemdescribed herein can be implemented to transmit command signals to thePCS as a series of bias signals. In an example, the signals can be inthe form of a command to charge or discharge the energy storage asset(s)over a period of time to participate in the energy market. For example,the command can signal the PCs to charge or discharge the energy storageasset over the period of about an hour. In another example, the signalscan be in the form of a signal to raise or lower the charge/dischargerate to participate in the regulation market. These command signals canbe sent over regular intervals of time such as twice or three times aday, each hour, about every 5 minutes, or every few seconds. Forexample, the regulation market operator may send commands to requestchanges in the charge/discharge rate at 2-second time intervals.

In an example, an apparatus or system herein can have a communicationlink or interface with a feed that provides the forecast LMP pricesand/or the actual LMP prices. In another example, an apparatus or systemherein can have a communication link or interface with a feed thatprovides the forecast LMP prices and/or the actual LMP prices, themetering inputs from the transportation system substation, the ARCOMinterface, and the PCS, in order to generate a command signal that issent to the PCS. A command signal can be configured to provide a setpoint value as a percentage of conversion capacity, either to charge ordischarge the energy storage asset, or a ‘zero’ corresponding to noenergy being converted (no charge, no discharge). As a non-limitingexample, the energy storage asset can be a battery that receives acommand to charge (from a DC bus) or to discharge (to a DC bus).

An example apparatus or system herein can be used to compute the valueof the set point from the various parameters that are input to theapparatus system, such as but not limited to the forecast LMP pricesand/or the actual LMP prices for the energy market and/or the regulationmarket price. The state of charge (SOC) of the battery energy storageassets also can be input to the apparatus or system.

Power Control System

In an example, a system described herein includes a PCS. The PCS can beused to control a converter, such as but not limited to a DC-to-DCConverter. The converter can be used to control the charge and dischargeof the energy storage asset. For example, the PCS can control theconverter based on the operating schedule generated by an optimizermodule. Implementation of the operating schedule can cause thecharging/discharging of the energy storage asset according to the setpoint provided by the operating schedule, the status of other subcomponents (including one or more other energy storage assets, otherconverters, the DC bus voltage), and the occurrence of the regenerativebraking

During a time period that an example system herein is participating inthe wholesale energy market, the PCS can be caused to execute theinstructions based on the operating schedule and drive the converter tocharge or discharge the energy storage asset(s) according to the desiredset point. During a time period that an example system herein is notparticipating in the wholesale energy market, the PCS can be caused tomonitor the DC bus voltage in real time to detect the signature ofregenerative braking. In this implementation, the PCS can be caused todrive the DC-to-DC Converter in order to maximize the energyrecuperation and charge the energy storage asset accordingly. The PCSconsiders other parameters, as described above, and takes them intoaccount when capturing the regenerative braking energy: The PCS also canbe used to monitor other parameters of the example system, including thestate of charge (SOC) of the energy storage asset, the ability of theDC-to-DC Converter to react (including for temperature management andany alarms.

Measurement and Verification

In an example system, the substation can include equipment fordetecting, measuring or otherwise quantify the energy flows from theregenerative braking event(s), in AC and/or DC current mode:

The AC energy can be measured using a meter(s). In an example, themeter(s) is provided by the power utility and is called a “datacollector”.

The DC energy can be measured at several places, such as but not limitedto, at the output of the rectifiers and at the DC breakers (called“feeders” as they feed power to the various tracks). Both voltage andcurrent may be displayed on the front of the feeder cabinet.

The existing DC current shunts may have a great accuracy, of about0.25%. With the addition of Analog-to-Digital (A to D) converters,capable of communicating using a communication protocol, such as but notlimited to the MODBUS® (The Modbus Organization, Hopkinton, Mass.)serial communications protocol, the overall accuracy can remain withinthe required 2%. The measurement chain may be calibrated to assess itsaccuracy.

The voltage and current readings can be transferred to the PCS using theModbus communication protocol, through the converters.

The power going through each feeder can be computed according to theexpression: power=voltage×current.

The PCS can be configured to time-stamp the received data andtemporarily store the values, using a remote time server to synchronizeits internal clock periodically. The data can be transferredperiodically to an example system herein to be archived. An examplesystem herein can be configured to capture, store and share data, asallowable in the regulation market.

The optimization solution can provide precise data regarding the flow ofenergy going to the train tracks (for example, when an energy storageasset is used to push a train) or coming from the train tracks (forexample, during a regenerative braking event). In an exampleimplementation, a method includes a computation procedure to subtractthe energy going directly from a rectifier to the train tracks.

In an example, the AC metering can be configured to read and store theenergy flow at regular time intervals, e.g., every 15 seconds, the PCScan be configured to read and store the DC energy data at anotherregular time interval (e.g., twice per second). A degree of accuracy ofthe data can be achieved by increasing the frequency of the readings.Higher degrees of accuracy are beneficial for the regenerative brakingenergy capture events, since they are fairly short-lived. For example,they can last about 8 seconds to about 15 seconds only. An exampleapparatus or system herein can be configured to store the data inconnection with the regenerative braking event at the regular timeintervals.

That is, discrete power measurements can be made independently of thePCS or the optimization system. The data can be flowed through the PCS,where it can be time-stamped, and stored in a memory of the system. Assuch the PCS can be configured to act as a communication conduit. In anexample, the quality of the measured values can be assessed bycalibrating probes. In another example, the internal clock can besynchronized with a more accurate device, such but not limited to remoteor a local network time server.

Optimizer Module

An optimizer module that can be implemented to generate an operatingschedule according to the example apparatus, systems and methods hereinis described. For example, the module can be implemented using aprocessing unit to determine the optimal utilizations of theregenerative braking energy and the energy storage assets to providedemand response energy that can be committed to a regulation marketand/or an energy market in (wholesale) electricity markets. The exampleapparatus, systems and methods provide the capability of modeling theregenerative braking energy production characteristics from electrictrain traction systems, or the regenerative braking energy productioncharacteristics from carriage systems (such as elevator systems or othermoving compartments), each in conjunction with energy storage asset(s)such as but not limited to electric batteries. As a non-limitingexample, a generated operating schedule may direct that the energystorage assets be charged from the retail energy supplies over a certaintime period and be discharged later when the LMPs are higher than athreshold amount and/or when the regenerative braking energy causesexcess charge accumulation of the energy storage assets above a certainpredetermined amount.

According to the example apparatus, systems and methods herein, theenergy storage assets are used to capture the excess regenerativebraking energy that would otherwise be emitted as waste heat in thetransportation system or the carriage system. The captured energy isstored in the energy storage asset, and may be used for voltage supportson the electric power traction lines or the carriage system. The otherfunction of the energy storage asset that is facilitated by thegenerated operating schedule is to provide demand response energy and/orregulation services to the wholesale electricity market.

According to the example apparatus, systems and methods herein, theregenerative braking energy capturing process is managed using the PCSbased on the implementation of the generated operating schedule. Anexample apparatus or system herein that includes an optimization moduleis used for generating the optimized schedules for the energy storageassets to maximize the overall benefits for the energy customer. Theoverall benefits of the optimal operating schedule includes power supplysavings as well as possible derived revenues from providing demandresponse energy, regulation services, or both, to a wholesaleelectricity market. The example apparatus or system herein that includesan optimization module determines the optimal energy storage asset(s)operating schedules based, e.g., on hourly forecasts for wholesaleelectricity market energy LMPs and/or regulation market clearing prices,regenerative braking energy availability, and the energy storageasset(s) operational characteristics. In an example, the power supplycontracts may provide certain limitations on the operation of the energystorage asset(s).

Once the optimal energy storage asset(s) operating schedules aredetermined, an example system or apparatus herein can be configured tosend control signals to the PCS controlling the operations of the energystorage asset(s). These control signals can be in the form of signalssent at regular time intervals, such as but not limited to five-minutesignals, indicating the desired state of one or more of the energystorage assets. As a non-limiting example, the control signals canindicate the desired charge/discharge states and the expected storagelevel (state of charge) of any battery assets on a five-minute timeinterval basis. In the case of regulation services being provided,system or apparatus herein can be configured to communicate energystorage asset control signals at shorter time intervals, such as but notlimited to four-second or two-second time intervals, to the PCS systemfor its controls on the energy storage asset, i.e., changing a rate ofcharging/discharging the energy storage asset(s).

In an example implementation, the PCS system can be configured tocommunicate back to the example system or apparatus the real-time actualoperating states of the energy storage asset. The energy storage asset'sreal-time operating state data can include the current state of chargeand storage levels. The real-time operating state data can be returnedas in input parameter to the optimizer module and, in a feedbackprocess, be used in the generation of another optimal operating schedulefor the energy storage asset.

In an example implementation, operation of the energy storage assets ofa transportation system or carriage system according to an exampleoperating schedule described herein can maximize cost savings andenvironmental benefits to an energy customer by recycling theregenerative braking energy to reduce electricity consumption. Therevenues may be maximized from deriving revenues from demand responseservices to the wholesale electricity markets.

Controllable Resources and Controllable Energy Assets in an Optimization

In an example system, method and apparatus herein, the operation ofcontrollable resources and controllable energy assets can be directedaccording to the operating schedule in order to achieve the objective ofsignificant increase in net energy-related benefits and/or significantdecrease in net energy-related costs. Non-limiting examples ofcontrollable energy asset that can be controlled according to anoptimizer-generated operating schedule include the excess regenerativebraking energy, the energy supplies from power supply feeders, and thecharge/discharge of the energy storage asset. Following is a descriptionof non-limiting examples of the modeling of controllable energy assetsand controllable resources. While the modeling is described based onelements and components of a transportation system, it is understoodthat the modeling also applies to equivalent elements and components ofa carriage system.

Excess Regenerative Braking Energy: Virtual Generator

In an example implementation, the amount of energy available forcharging the energy storage asset(s) may be reduced by a certain amountfrom the regenerative braking energy generated based on the brakingmotion.

For example, for an example transportation system that includes pairs ofcoupled train cars (married pair of train cars), each married pair canregenerate a current of up to about 2750 A during a single brakingevent. The traction power line voltage can be limited to maximum 800 VDC(‘clipping’), so the current due to the regenerative braking energy maybe reduced to maintain the power line voltage within the limit. Inaddition, some of this regenerative braking energy may be used by theauxiliary systems of the train car (including the compressor, the HVAC,and the lighting), used to assist a train accelerate to exit the station(i.e., provide a push), and/or some can be absorbed by the naturalreceptivity of the traction power line. The remaining amount of energyis referred to herein as the excess regenerative braking energy and maybe captured by the energy storage asset as stored energy (whichotherwise may be emitted as waste heat).

The regenerative braking event can take place in a very short timeinterval. For example, for a train traveling at a speed of about 49miles per hour, the braking time is about 15 seconds (referred to hereinas the regenerative braking time window). Within the regenerativebraking time window, regenerative braking energy may be produced. Theregenerative braking energy can be represented in a plot of energy vs.time as a triangular shape. The power from regenerative braking rises toits peak value in approximately 1 second following the start of thebraking action, then can decrease linearly while the train slows down.It ends abruptly at the end of the regenerative braking time window. Forthe example of a train, this occurs when the mechanical brakes areapplied just before stopping.

The number of braking events per day in a transportation system can becomputed as the number of trains passing by a traction power substationwhere the energy storage asset is installed and stopping at the adjacentpassenger stops. The amount of recoverable regenerative braking energycan vary with the distance between trains (i.e., impedance of theconductors between the trains), which is proportional to the headway.For a non-limiting example train line passing a power substation,described in the “Example Implementation” section hereinbelow, there canbe as many as 378 trains running per day in both directions per workingday and 204 trains per weekend day. The number of trains passing a powersubstation for any other train lines can be computed according to theexamples described herein (including in the “Example Implementation”section herein below). Each stop of these running trains producesregenerative braking energy. The regenerative braking energy may thenlead to excess energy for capturing with the energy storage asset at thetimes where the voltage conditions on the traction power line arefavorable. Therefore, the regenerative braking power signals can behighly intermittent and take place in very short time windows.

With the approximation that the amount of excessive regenerative brakingpower and energy signals over time for a given time horizon into thenear future at a specific substation can be pre-determined in terms ofthe train schedules, and the substation locations and traction powerline voltage control operations, the excessive regenerative brakingpower and energy signals can be represented as a virtual generator withthe generator model in an example system, method and apparatus herein.In an example, an energy generating capacity of excess regenerativebraking energy for a transportation system may be determined accordingto each substation or location with at least one energy storage assetbattery installed when the regenerative braking power and energy signalsare significantly different.

Furthermore, it is observed that the amount of energy accumulated in theenergy storage assets based on the excess regenerative braking energyincreases cumulatively over time. For example, the state of charge ofthe energy storage asset can increase by an amount of about 10%, about20%, about 30% or higher over the time periods that it is being chargedusing the excess regenerative braking energy and is being committed to aregulation market. Such an excess accumulated charge on the energystorage assets may cause them to become too highly charged toparticipate viably in the regulation. That is, an energy storage assetthat is charged to only about a half of its capacity can be more usefulin a regulation market (that the regulation operator has benefit of thepossibility for charging or discharging beyond this point inparticipating in the regulation market. In an example implementation,the optimizer module can be used to generate an operating schedule suchthat the excess charge on the energy storage assets can be committed tothe energy market at specific time periods to discharge some of theaccumulated excess charge and committed to the regulation market atother time periods. Implementation of the operating schedule generatedby the optimizer for such a co-optimization of the use of the energystorage assets in the regulation and energy markets can be used toderive an amount of energy-related revenue. In another exampleimplementation, the optimizer module can be used to generate anoperating schedule such that the energy storage asset can be dynamicallypartitioned such that it participates in the energy market and theregulation market in such a manner that the excess charge is fullyexploited to derive an amount of energy-related revenue.

In an example, a system, apparatus and method herein includes agenerator model that can be used to model the regenerative brakingenergy as a virtual generator. Non-limiting examples of the virtualgenerators are described in the “Example Implementation” sectionhereinbelow, as generated based on the generator static data and thegenerator time dependent data.

In an example transportation system, the regenerative braking energy canbe produced in about a 15-second time window. In an example, anapparatus, method or system herein that includes the optimizer modulemay not need to apply the time granularity at such a fine level (i.e.,at a similar timescale) to determine the optimal operating schedules forthe energy storage asset (such as but not limited to charge/dischargeschedules). The maximum available amount of excessive regenerativebraking energy for each time interval can be introduced as a parameterin the optimizer module. The maximum available amount of excessiveregenerative braking energy (referred to herein as parameter MaxMWh) maybe pre-determined based on the train start/stop schedules around thesubstation. Parameter MaxMWh represents the maximum amount of excessiveregenerative braking energy that may be scheduled for each timeinterval.

Energy Supplies from AC Power Supply Feeders

In an example implementation, energy supplies from the AC power linefeeders at a substation where an energy storage asset such as a batteryis installed can be used as another power source. The energy suppliesfrom the AC power line feeders can be used in addition to, or insteadof, the excessive regenerative braking energy, to charge the energystorage asset when it is economic to do so (i.e., net-energy relatedrevenue may be derived).

The energy supplies from the AC power line feeders can be used asanother controllable variable in optimizing the operating schedules fordemand response energy and regulation services. A non-limiting exampleof a supply contract model is included as a table in the “ExampleImplementation” section hereinbelow.

Electric Batteries

Electric batteries are examples of controllable energy asset and theircharge and discharge operations can be optimized to determine the mosteconomic utilizations of the battery storage capabilities to provideenergy and regulation services to the wholesale electricity market as ademand resource.

Non-limiting examples of model attributes for battery static parametersand battery time-based parameters are included as tables in the “ExampleImplementation” section hereinbelow.

Math Formulation for Optimizing Regenerative Braking Energy Utilization

Following is a non-limiting example methodology of optimizing theregenerative braking energy utilizations, in conjunction with theelectric battery storage capabilities, to provide economic demandresponse energy and/or regulation services to the wholesale electricitymarkets. A non-limiting example of an “objective cost function” (alsoreferred to as a “objective function”) is defined to express netenergy-related benefits, based on a variety of possible energy-relatedcosts and energy-related revenues in connection with scheduling andoperation of controllable energy assets in a transportation systemenvironment. An optimization procedure determines a suggested operatingschedule for the controllable energy assets that maximizes the objectivefunction defining the net energy-related benefits.

It should be appreciated that in the mathematical formulation discussedbelow, an objective function may be alternatively expressed as a “netenergy-related cost” rather than a net energy-related benefit. Usingsuch an alternative expression for the objective function, anoptimization procedure would then determine a suggested operatingschedule for the controllable energy assets that minimizes the objectivefunction defining the net energy-related costs.

A number of energy-related costs and energy-related expenses may betaken into consideration in the formulation of an objective function(expressed either as a net energy-related benefit or a netenergy-related cost). Examples of energy-related costs include, but arenot limited to, the retail supply cost to an energy customer (a “retailcustomer”) from purchasing electricity from a retail electricitysupplier, the cost of operating and maintaining one or more energystorage assets (e.g., batteries), and an emission cost associated withcausing various emissions as a result of operating controllable energyassets. Examples of energy-related revenues include an economic demandresponse revenue from the wholesale energy market, e.g., as a result ofvoluntary curtailment of energy use relative to a customer baseline orCBL based on business as usual or BAU conditions, as well as an economicdemand response revenue from the wholesale regulation market.

It should be appreciated, however, that in various implementations, notall of the above-identified energy-related costs and energy-relatedrevenues need to be included in a particular objective function for aparticular application. For example, in one implementation, a suggestedoperating schedule for controllable energy assets may be determined bymaximizing an objective cost function representing a net energy-relatedbenefit (or alternatively by minimizing an objective cost functionrepresenting a net energy-related cost), wherein the objective costfunction considers only demand response revenue from a wholesaleregulation market and battery operation and maintenance costs, and doesnot consider costs or revenues associated with a demand response energymarket, retail supply costs, and emission costs (e.g., in connectionwith some implementations of one or more energy storage assetsassociated with transportation operations, an objective function in agiven application may be only concerned with revenue from regulationmarkets and costs associated with the energy storage asset(s)). Anobjective cost function also may include a “convenience cost”representing an economic value corresponding to a willingness of theenergy customer to change energy-related behavior by choosing to adopt asuggested operating schedule for the purposes of earning revenues fromone or more wholesale electricity markets. Such a convenience cost alsomay be considered as an “indirect” energy-related cost.

In sum, the discussion below relating to definition and utilization ofan objective function so as to determine suggested operating schedulesfor controllable energy assets (e.g., one or more energy storage assetsassociated with transportation operations) is provided primarily forpurposes of illustration, and is not intended to be limiting, as otherobjective functions are suitable in other example implementations of theconcepts disclosed herein.

Modeling the Objective Function

An example objective function is described hereinbelow in connectionwith optimizing regenerative braking energy. The various expressions fordetermining the objective function can be implemented using a processingunit executing machine-readable instructions.

Optimizing regenerative braking energy utilizations to provide demandresponse (DR) services to wholesale electricity markets could maximizethe overall financial benefits from energy and regulation servicesprovided to wholesale electricity markets, minimize the retail energysupply costs, and minimize CO₂, SO₂ and NOx emissions, according to theexpressions below:

$\begin{matrix}{{maximize}\begin{Bmatrix}{{DREnergyRevenue} + {DRRegRevenue}} \\{{- {RetailSupplyCost}} - {EmissionCost} - {BatteryOMCost}}\end{Bmatrix}} & (1)\end{matrix}$

where the parameters are described as follows

-   -   DREnergyRevenue: The total revenue for providing DR energy to        the wholesale electricity market over the scheduling horizon        (solution variable);    -   DRRegRevenue: The total revenue for providing DR regulation to        the wholesale electricity market over the scheduling horizon        (solution variable);    -   RetailSupplyCost: The total retail supply cost over the        scheduling horizon (solution variable);    -   EmissionCost: The total cost of causing CO2, SO2 and NOx        emissions over the scheduling time horizon (solution variable);    -   BatteryOMCost: The total cost of operating and maintaining the        battery over the scheduling time horizon (solution variable). It        may include the loss of battery life cost component.        These terms are defined below:

$\begin{matrix}{\mspace{79mu} {{DREnergyRevenue} = {\sum\limits_{t}\; \left\lbrack {{DRMW}_{t}*\left( {{LMP}_{t} - {GRate}} \right)} \right\rbrack}}} & (2) \\{\mspace{79mu} {{DRRegRevenue} = {\sum\limits_{t}\; \left\lbrack {{RegMW}_{t}{\,{*{RegMCP}_{t}}}} \right\rbrack}}} & (3) \\{\mspace{79mu} {{RetailSupplyCost} = {\sum\limits_{t}\; \left\lbrack {{RetailSupplyMW}_{t}*{RetailRate}_{t}} \right\rbrack}}} & (4) \\{\mspace{79mu} {{EmissionCost} = {\sum\limits_{t}\; \begin{bmatrix}{{{CO}2{CostRate}}*{{CO}2{Produced}}_{t}} \\{{+ {{SO}2{CostRate}}}*{{SO}2{Produced}}_{t}} \\{{+ {{NO}{xCostRate}}}*{{NO}{xProduced}}_{t}}\end{bmatrix}}}} & (5) \\{{BatteryOMCost} = {\sum\limits_{t}\; \left\lbrack {{BatteryOMCostRate}_{t}*{{Discharge}{MW}h}_{t}} \right\rbrack}} & (6)\end{matrix}$

-   where DRMW_(t): The DR energy MW offered to the wholesale    electricity market at time interval t (solution variable);

LMP_(t): The Wholesale electricity market LMP at the DR zone at timeinterval t;

-   -   GRate: The generation rate part of the retail rate;    -   RegMW_(t): The DR regulation MW offered to the RTO market at        time interval t (solution variable);    -   RegMCP_(t): The RTO market regulation market clearing price for        the DR zone;    -   RetailSupplyMW_(t): The retail supply MW projected to meet the        load at time interval t (solution variable);    -   RetailRate_(t): The power supply contract price at time interval        t.    -   CO2CostRate: The $/lbs (or ton) of CO2 emission;    -   CO2Produced_(t): The amount of CO2 emission caused for time t        (solution variable);    -   SO2CostRate: The $/lbs (or ton) of SO2 emission;    -   SO2Produced_(t): The amount of SO2 emission caused for time t        (solution variable);    -   NOxCostRate: The $/lbs (or ton) of NOx emission;    -   NOxProduced_(t): The amount of NOx emission caused for time t        (solution variable);    -   BatteryOMCostRate: The $/MWh of battery discharge;    -   DiscahrgeMWh_(t): The scheduled discharge MWh for time t        (solution variable).

The DRMW_(t) is the difference between the CBL and the actual totalload:

DRMW_(t)=max(0,CBL_(t)−LoadMW_(t)−BatteryCharge_(t)  (7)

where CBL_(t): Customer baseline load;

-   -   LoadMW_(t): Total fixed load to serve at the DR registered        metering location at time interval t;    -   BatteryCharge_(t): Scheduled charge MW of the battery at time        interval t (solution variable).

The Optimization Constraints

The optimal regenerative braking energy utilization scheduling problemis maximized subject to the following operational limit constraints.

A. Demand-Supply Balance Constraints

The power demand plus battery charging load may be met with the retailsupply, battery discharge and regenerative braking power, expressed asfollows:

LoadMW_(t)+BatteryCharge_(t)=RetailSupplyMW_(t)+BatteryDischarge_(t)+GenMW_(t)+RegenBrakingMW_(t)  (8)

-   where BatteryDischarge_(t): Scheduled discharge MW of the battery at    time interval t (solution variable);    -   RetailSupplyMW_(t): Scheduled retail supply MW at time interval        t (solution variable);    -   GenMW_(t): Virtual generation MW from the excessive regenerative        braking energy at time interval t (solution variable);    -   RegenBrakingMW_(t): Pre-scheduled regenerative braking MW to        serve the fixed load at time interval t.

B. Supply Contract Constraints

The supply contract constraint can be applied to ensure that thescheduled supply MW value does not exceed the physical limitation thatmay be delivered to the substation:

RetailSupplyMW_(t)≦RetailSupplyMax_(t)  (9)

C. Virtual Generator Constraints for Excessive Regenerative BrakingPower

The virtual generator output may be scheduled between 0 MW and themaximum MW amount representing the pre-determined range of availableexcessive regenerative braking power at any time:

MinGen_(t)≦GenMW_(t)≦MaxGen_(t)  (10)

-   where MinGen_(t): Minimum generation output of the virtual    generator; typically set to 0;    -   MaxGen_(t): Maximum generation output of the virtual generator;        set to the maximum possible MW value of the excessive        regenerative braking power for time interval t;

The total amount of energy that can be supplied from the virtualgenerator is constrained to the maximum total amount of availableexcessive regenerative braking energy pre-determined based on the trainschedules, starts/stops, and the voltage conditions on the tractionpower lines at the substation where the battery is installed. Therefore,the generation MW variable is further constrained with the maximumavailable excessive regenerative braking energy during time interval t,as follows:

GenMW_(t)*IntervalDuration(t)/60≦MaxMWh_(t)  (11)

-   where MaxMWh_(t): Maximum of available excessive regenerative    braking energy during time interval t;

D. Regulation and Battery Operational Constraints

Battery charge and discharge must be within its charge and dischargerate limits:

MinChargeRate≦BatteryCharge_(t)*IntervalDuration(t)/60≦MaxChargeRate  (12)

MinDischargeRate≦BatteryDischarge_(t)*IntervalDuration(t)/60≦MaxDischargeRate  (13)

Battery storage levels are determined based on initial battery storagelevel, charge and discharge operations:

$\begin{matrix}{{StorageLevel}_{t} = {{{{{StorageLevel}_{t - 1}}^{*}\left( {1 - {LossRate}} \right)}^{*}{{IntervalDuration}(t)}} + {BatteryCharge}_{t} - \frac{{BatteryDischarge}_{t}}{Efficiency}}} & (14)\end{matrix}$

Since regulation services are to be provided from the battery, thebattery storage level is maintained at a value that can meet thespecifications to provide the scheduled regulation services in both theupward and downward directions:

StorageLevel_(t)+RegDnDeployFactor_(t)*RegMW_(t)*RRT/60≦MaxCapacity  (15)

StorageLevel_(t)−RegUpDeployFactor_(t)*RegMW_(t)*RRT/60≧MinCapacity  (16)

-   -   Where RegUpDeployFactor_(t): Regulation Up deployment factor        representing the amount of the deployed upward regulation        relative to the regulation assignment;    -   RegDnDeployFactor_(t): Regulation Down deployment factor        representing amount of the deployed downward regulation relative        to the regulation assignment;    -   RRT: Regulation response time, which is typically 5-minutes.        Further, the regulation capability in 5 minutes are also        constrained with the max and min discharge rates:

MinDischargeRate≦RegMW_(t)*RRT/60≦MaxDischargeRate  (17)

E. Battery Charge and Virtual Generator Scheduling Constraints

Since the virtual generator represents the excess regenerative brakingenergy, this amount of available excess regenerative braking energy isemitted as waste heat when the battery is not charging. Therefore, theschedule of utilizing the excessive regenerative braking energy isdependent of the charging operations of the battery.

A. The virtual generator and the battery charging operation dependencycan be expressed as follows:

GenMW_(t)≦BatteryCharge_(t)  (18)

Traction Power Line Voltage Security Constraints

In an example, the traction power line voltage security constraints maybe included. For example, the DC power flow models of the traction powersystem may be implemented so that the traction power line voltages ismodeled as part of the optimization and the optimization model includesthe algorithm to determine the regenerative braking energy capture inconjunction with the voltage conditions on the traction power lines.

CBL Approaches for a Transportation System

In an example, the historical load data may be used to project the CBL.In another example, the CBL may be derived by registering the battery asa behind-the meter generating resource. In this example, the batterydischarge operations are metered and used for settlements of the energyand regulation services provided to the wholesale electricity market.

In another example, the CBL may be derived based on actual metered loadsthat are adjusted using the actual metered battery charge and dischargesafter the fact, and the adjusted historical load data that is used tocompute DR energy and regulation services actually delivered to thewholesale electricity market.

Solution Data to SCADA

The solution data by the optimizer module can be transferred to theSCADA for communicating with the PCS for real-time operations of theenergy storage asset to provide the economic DR and regulation servicesto the wholesale electricity market.

Market Demand Resource Solution Data Non-limiting example of demandresource solution data is provided in the table below.

Data Item Description Destination of Data  1) MktDRName Market demandresource name or ID Simulation  2) IntervalID Interval name or IDSimulation  3) DRCapability Projected MW amount of power available foroffering to Simulation to SCADA the RTO market, in MW  4) TotalGen Totalgeneration of the market demand resource, in MW Simulation  5) TotalLoadTotal load of the market demand resource, in MW Simulation  6)SupplyContract_MW Total MW schedule by supply contract Simulation  7)OperationCost Cost to achieve the demand resource load reductionSimulation  8) Penalty Penalty due to deviating from target temperatureor Simulation violating operating limits.  9) DRValue Demand resourceforecasted market value Simulation 10) DRValueAccrual Market demandresource forecasted market value Simulation accrual, penalty costexcluded 11) BAUCBL Business as usual CBL simulation results Simulationto SCADA 12) Emission Produced emission in kg (or lb) Simulation 13)MktDRIndex Carry-thru from input Simulation 14) IntervalIndex Carry-thrufrom input Simulation 15) Cox Produced COx emission of the Market DemandSimulation Resource 16) NOx Produced COx emission by the Market DemandSimulation Resource 17) Sox Produced COx emission by the Market DemandSimulation Resource

Generator Solution Data

Non-limiting example of general solution data is provided in the tablebelow.

Data Item Description Destination of Data  1) ClientName Client name orID Simulation  2) PhysicalGeneratorName Physical generator nameSimulation  3) GeneratorName Logical generator name or ID Simulation  4)IntervalID Time interval ID Simulation  5) Status 1: Generator isrunning Simulation 0: Generator is offline  6) GenOutput Generationoutput in MW, MBtu or other unit Simulation to SCADA  7) GenCostGeneration cost of producing GenOutput for the Simulation given interval 8) FuelConsumption Fuel consumption Simulation  9) Emission Producedemission Simulation 10) ClientIndex Carry-thru from input Simulation 11)PhysicalGeneratorIndex Index for PhysicalGeneratorName in SimulationSimulation 12) GeneratorIndex Carry-thru from input Simulation 13)IntervalIndex Carry-thru from input Simulation 14) COx COx emission inlbs (ton, etc) produced by the Simulation generator 15) NOx NOx emissionin lbs (ton, etc) produced by the Simulation generator 16) SOx SOxemission in lbs (ton, etc) produced by the Simulation generator

Storage Solution Data

Non-limiting example of storage solution data is provided in the tablebelow.

Data Item Description Destination of Data 1) ClientName Client name orID Simulation 2) StorageName Unique storage name or ID Simulation 3)IntervalID Interval ID Simulation 4) StorageLevel Storage energy storagelevel Simulation to SCADA 5) ChargeMW MW energy consumption ofSimulation to SCADA charging the storage 6) DischargeMW MW energyproduced due to Simulation to SCADA storage discharge 7) ClientIndexCarry-thru from input Simulation 8) StorageIndex Carry-thru from inputSimulation 9) IntervalIndex Carry-thru from input Simulation

Other Data from SCADA to Simulation

As part of the DR operations and settlement for the actualimplementation of a suggested operating schedule, the following data maybe sought from the SCADA which in turn may be acquired from the PCS:

The actual real-time load at the main feeder location at which the DR isregistered for the RTO market.

The actual total amount of regenerative braking energy.

The total amount of excessive regenerative braking energy.

Other Data for the Optimization

The following data can be used for the optimization:

Wholesale electricity market LMPs for the optimization horizon.

Regulation market clearing prices for the optimization horizon.

Projected total load demand for the defined DR footprint.

Acronyms

AC Alternating Current

A Ampere

ARCOM ARCOM Control Systems

BMS Battery Management System

DC Direct Current

DNP3 Distributed Network Protocol Version 3

ES Energy Storage

ESS Energy Storage System

EST Eastern Standard Time

FAT Factory Acceptance Test

HMI Human-Machine Interface

HVAC Heat Ventilation Air-Conditioning

IBC International Building Code

IEEE Institute of Electrical and Electronics Engineers

IP Internet Protocol

kW Kilowatt

kWh Kilowatt-hour

LLC Limited liability corporation

LES Load Serving Entities

MW Megawatt

MWh Megawatt-hour

PCS Power Control System

PECO PECO Energy Company

PEDA Pennsylvania Energy Development Authority

PHEV Plug-In Hybrid Electric Vehicles

PJM PJM Interconnection LLC, the Regional Transmission Organization

PQ Power Quality

PS Peak Shaving

RFQ Request for Quote

RTO Regional Transmission Organization

SAT Site Acceptance Test

SCADA Supervisory Control and Data Acquisition

SOC State Of Charge

TPSS Traction Power Substation

UL Underwriters Laboratories Inc.

USDOT United States Department of Transportation

V Volt

VDC Volts Direct Current

Simulation Optimization machine readable instructions

Example Implementation

Example Train Schedule & Headways

An example train schedule of arrival and headways into a train stationis described. The number of trains passing by a TPSS and stopping at theadjacent passenger stops can be used to compute the number of brakingevents to occur for any given day. The amount of recoverableregenerative braking energy can vary with the distance between thetrains, affecting the impedance of the conductors between the trains,which is proportional to the headway. The headway can result in areduction of the amount of the regenerative braking energy that can beused to charge the energy storage asset.

Week Days Saturday & Sunday From 5:00 to 6:17 am: 9 trains From 5:00 to6:16 am: 5 trains From 6:24 to 9:12 am From 6:31 to 7:31 am Headway: 4minutes (each direction) Headway: 15 minutes (each direction) From 9:17am to 1:35 pm: From 7:44 am to 8:44 am: Headway: 6 minutes (eachdirection) Headway: 12 minutes (each direction) From 1:40 pm to 3:29 pm:From 8:55 am to 8:35 pm: Headway: 5 minutes (each direction) Headway: 10minutes (each direction) From 3:32 pm to 5:40 pm: From 8:44 pm to 9:20pm: Headway: 4 minutes (each direction) Headway: 12 minutes (eachdirection) From 5:45 pm to 6:05 pm: From 9:35 pm to midnight: Headway: 5minutes (each direction) Headway: 15 minutes (each direction) From 6:11pm to 6:26 pm: Headway: 8 minutes (each direction) From 6:35 pm to 8:45pm: Headway: 10 minutes (each direction) From 8:56 pm to midnight:Headway: 12 minutes (each direction) Total trains per day, bothdirections: Weekdays: 378; Saturday and Sunday: 204

Virtual Generator

A generator model can be used to model the regenerative braking energyas a virtual generator. As non-limiting examples, virtual generators canbe developed from a generator model based on the generator static dataand the generator tie dependent data in the following two tables.

Generator Static Data

Applicable to the virtual generator for excessive Generator AttributesDefinitions regenerative braking Sources of Data  1) GeneratorNameGenerator name or ID Yes Modeling  2) IncrCost Incremental energy cost,Yes. may set to 0. Static data Modeling in $/MWh  3) UpRampLimit Up ramprate limit, Yes. May set to very large value Modeling MW/hr  4)DnRampLimit Down ramp rate limit, Yes. May set to very large valueModeling MW/hr  5) MinRunTime Minimum run time, in hr No, Set to 0.Static data  6) MinDownTime Minimum down time in hr No, set to 0. Staticdata  7) StartupTime Time to start in hr No, set to 0. Static data  8)InitialOnHours Initial hours online (+) or No, set to 0. Static dataoffline (−)  9) StartupCost Cost per startup, in $ No. Static data 10)OpMaintCost Operation & maintenance Yes. Static data Modeling cost in$/h 11) FuelConsumptionRate In Gal/MWh or No. Static data Gal(ton)/MBtu12) COx_Rate Decimal in lbs (tons, No. Static data etc)/Gal (cfm, ton,etc) of fuel. 13) NOx_Rate Decimal in lbs (tons, No. Static dataetc)/Gal (cfm, ton, etc) of fuel. 14) SOx_Rate Decimal in lbs (tons, No.Static data etc)/Gal (cfm, ton, etc) of fuel. 15) MaxRunTime Maximumrun-time, in hr No. Static data

Generator Time Dependent Data

Applicable to the virtual generator for excessive Data Item Descriptionregenerative braking Sources of Data  1) GeneratorName Generator name orID Yes Modeling  2) IntervalID Time interval ID Yes Modeling  3)Available 1: Generator is 100% available Yes. Flag indicating whetherSCADA to Modeling to generate electricity there is any excess 0:Generator is not available to regenerative braking energy generateelectricity in MW to capture 0< and <1: the generator is partiallyavailable  4) MinGen Minimum generation capacity, Yes. Minimum amount ofSCADA to Modeling in MW excess regenerative braking Manual entries orenergy in MW to capture EDSA to Modeling  5) MaxGen Maximum generationcapacity, Yes. Maximum amount of SCADA to Modeling; in MW excessregenerative braking Manual entries or energy in MW that may be EDSA toModeling captured  6) FixedGen 1: Generator is in fixed Yes. Fixedamount of excess SCADA to Modeling; generation mode regenerative brakingenergy Manual entries 0: Generator is dispatchable in MW to capture  7)FixedGenMW Fixed generator generation Yes. Flag indicating whether SCADAto Modeling; MW, used when FixedGen = 1 there is a fixed amount ofManual entries excess regenerative braking energy in MW to capture  8)MeteredGenMW Metered actual No. charge/discharge MW values  9)MeteredGenMW_Quality 0/1: 0 - Bad; 1 - Good No. 10) MaxMWh Maximum MWhof production Yes. Set to the maximum SCADA to Modeling; MWh value ofthe excessive Manual entries or regenerative energy for a EDSA toModeling scheduling interval. This is a new generator attribute.Interval data

Energy Supplies Contract Model

A non-limiting example of a supply contract model is shown in thefollowing table.

Contract Attributes Definitions Sources of Data 1) ClientName Clientname or ID. It may map to the Substation for which the Simulation powersupply contract may differ from those in other substation feeders in oneof the following numerical attributes 2) IntervalID Interval ID stringSimulation 3) BlockID Block ID string Simulation 4) BlockMWBreakPoint MWbreak-point of multiple block supply contract curve Simulation 5)BlockPrice Supply contract block price in $/MWh Simulation 6)SupplyContractIndex Index for the supply contract in Simulation denotedwith Simulation ClientName and IntervalID 7) COx_Rate Decimal in lbs(tons, etc)/MWh. The carbon emission rate of the Simulation supplycontract 8) NOx_Rate Decimal in lbs (tons, etc)/MWh. The nitrogenemission rate of Simulation the supply contract 9) SOx_Rate Decimal inlbs (tons, etc)/MWh. The sulfur emission rate of the Simulation supplycontract

Battery Model Attributes For Electric Batteries

Example battery model attributes are shown in the following table forbattery static parameters and battery time-based parameters.

Battery static parameters

Battery Attributes Definitions Sources of Data  1) ClientName Clientname or ID. It may map to the Substation for Simulation which the supplycontract may differ from those in other substation feeders  2)StorageName Unique storage (battery) resource name or ID Simulation  3)MinCapacity Minimum storage capacity, in MWh SCADA to Simulation;Simulation default  4) MaxCapacity Maximum storage capacity, in MWhSCADA to Simulation; Simulation default  5) LossRate Storage energystorage loss rate in percent per hour Simulation (%/hr)  6) EfficiencyEfficiency factor associated with charging and Simulation discharging,value in [0, 1]  7) MinChargeRate Minimum charge rate, in MWh/hre SCADAto Simulation; Simulation default  8) MaxChargeRate Maximum charge rate,in MWh/hr SCADA to Simulation; Simulation default  9) MinDischargeRateMinimum discharge rate, in MWh/hr SCADA to Simulation; Simulationdefault 10) MaxDischargeRate Maximum discharge rate, in MWh/hr SCADA toSimulation; Simulation default 11) InitialStorageLevel Initial (current)storage level of the storage, in SCADA to Simulation; MWh Not sure ifthe storage level is directly measurable. If not, SCADA needscalculations to track the storage level. 12) FixEndStorageLevel 0: Endstorage level is not enforced SCADA to Simulation; 1: fix end storagelevel, enforced as minimum end Simulation default storage level 13)EndStorageLevel End storage level, required only if SCADA to Simulation;FixEndStorageLevel = 1, in MWh Simulation default 14)EndStorageDeficitPenalty Penalty cost applied when storage end storagelevel Simulation is below EndStorageLevel under the condition thatFixEndStorageLevel = 1 15) ExceedCapacityPenalty Penalty cost associatedwith storage level exceeding Simulation capacity, in $/MWh 16)BelowMinCapacityPenalty Penalty cost associated with storage storagelevel Simulation below minimum capacity, in $/MWh 17)SimultaneousChargeDischarge 0: Simultaneous charge and discharge optionis off Simulation 1: Simultaneous charge and discharge option is on 18)StorageIndex Index for StorageName in Simulation Simulation 19)TypeOfStoredEnergy Electric: Input/Output is electricity (MW) SimulationThermal: Input/Output is thermal energy 20) BHMStorage 0/1: 0 - Storageresource is in front of the meter for Simulation the total load byclient location; 1 - Storage resource is behind the meter 21) OMCost$/MWh of discharge, used to reflect the loss of Simulation battery lifeand other battery operation & maintenance related costs. This is a newattribute to be added 22) TimeOnCharge +/− numeric value. Number ofminutes that the SCADA to Simulation. battery is on charging (+) ordischarging (−) mode SCADA may needs to track the time that the batteryhas been on charging or discharging mode.

Battery time based parameters

 1) StorageName Unique storage (battery) name or ID Sources of Data  2)IntervalID Interval string for each study period Simulation  3)Available 0: Storage is not available for the given interval SCADA toSimulation; 1: Storage is 100% available for the given Simulationdefault interval 0< and <1: the storage is partially available  4)FixedCharge 0: Storage charge rate is not fixed; 1: Storage SCADA toSimulation; charge rate is fixed Simulation default to non-fixed  5)FixedChargeRate Fixed storage charge rate, in MWh/hr SCADA to Simulation 6) FixedDischarge 0: Storage discharge rate is not fixed; 1: SCADA toSimulation; Storage discharge rate is fixed Simulation default tonon-fixed  7) FixedDischargeRate Fixed Storage discharge rate, in MWh/hrSCADA to Simulation  8) MinCapacity Minimum storage capacity by time, inMWh. SCADA to Simulation; Simulation default  9) MaxCapacity Maximumstorage capacity by time, in MWh. SCADA to Simulation; Simulationdefault 11) MeteredChargeMW Metered actual charge MW SCADA to Simulation12) MeteredChargeMW_Quality 0/1: 0 - Bad; 1 - Good SCADA to Simulation13) MeteredDischargeMW Metered actual discharge MW SCADA to Simulation14) MeteredDischargeMW_Quality 0/1: 0 - Bad; 1 - Good SCADA toSimulation

CONCLUSION

While various inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.

The above-described embodiments of the invention can be implemented inany of numerous ways. For example, some embodiments may be implementedusing hardware, software or a combination thereof. When any aspect of anembodiment is implemented at least in part in software, the softwarecode can be executed on any suitable processor or collection ofprocessors, whether provided in a single computer or distributed amongmultiple computers.

In this respect, various aspects of the invention may be embodied atleast in part as a computer readable storage medium (or multiplecomputer readable storage media) (e.g., a computer memory, one or morefloppy disks, compact disks, optical disks, magnetic tapes, flashmemories, circuit configurations in Field Programmable Gate Arrays orother semiconductor devices, or other tangible computer storage mediumor non-transitory medium) encoded with one or more programs that, whenexecuted on one or more computers or other processors, perform methodsthat implement the various embodiments of the technology discussedabove. The computer readable medium or media can be transportable, suchthat the program or programs stored thereon can be loaded onto one ormore different computers or other processors to implement variousaspects of the present technology as discussed above.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present technology asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present technology need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present technology.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, the technology described herein may be embodied as a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

1-29. (canceled)
 30. A method of generating energy-related revenue inconnection with operation of an electric rail system, the electric railsystem comprising at least one energy storage asset to storeregenerative breaking energy arising from the operation of the electricrail system, the method comprising: A) electronically determining asuggested operating schedule for charging and discharging of the atleast one energy storage asset based on optimization of an objectivecost function representing, at least in part, a demand response revenuefrom a regulation market; and B) controlling the at least one energystorage asset according to the suggested operating schedule.
 31. A powercontrol system to control at least one energy storage asset associatedwith an electric rail system, the at least one energy storage assetconfigured to store regenerative breaking energy arising from theoperation of the electric rail system, the power control systemcomprising: at least one communication interface to receive a suggestedoperating schedule for charging and discharging of the at least oneenergy storage asset, the suggested operating schedule being based onoptimization of an objective cost function representing, at least inpart, a demand response revenue from a regulation market; and acontroller, coupled to the at least one communication interface, tocontrol the at least one energy storage asset according to the suggestedoperating schedule received via the at least one communicationinterface. 32-81. (canceled)
 82. A method of generating energy-relatedrevenue in connection with operation of an electric rail system, theelectric rail system comprising at least one energy storage asset tostore regenerative breaking energy arising from the operation of theelectric rail system, the method comprising: A) electronicallydetermining a suggested operating schedule for charging and dischargingof the at least one energy storage asset based on optimization of anobjective cost function representing, at least in part, a demandresponse revenue from a wholesale electricity market; and B) controllingthe at least one energy storage asset according to the suggestedoperating schedule.
 83. A power control system to control at least oneenergy storage asset associated with an electric rail system, the atleast one energy storage asset configured to store regenerative breakingenergy arising from the operation of the electric rail system, the powercontrol system comprising: at least one communication interface toreceive a suggested operating schedule for charging and discharging ofthe at least one energy storage asset, the suggested operating schedulebeing based on optimization of an objective cost function representing,at least in part, a demand response revenue from a wholesale electricitymarket; and a controller, coupled to the at least one communicationinterface, to control the at least one energy storage asset according tothe suggested operating schedule received via the at least onecommunication interface. 84-94. (canceled)